Saved evidence report

Fieldwork / Missingness

Explore the saved analysis below. Controls filter this report; they do not rerun the analysis or retrieve source data.

Population: input · 12 evaluated source rows

Analysis: 12 rows; presence=per_row

Search coverage and limits

Search limits restrict what was tested or retained. Untested work is unknown. Display limits below only restrict this report.

missingness: 3/3 pairs evaluated

missingness: 2/2 contexts retained

missingness: 3/3 signatures retained

pair candidates	3
pairs evaluated	3
signatures total	3
signatures shown	3
signature omitted units	0
signature omitted rows	0
contexts total	2
contexts shown	2
entity missing key rows	0

Visual summary

The visual summary has its own display limit of up to 12 items per list. Browse the included evidence below.

Inspect findings

availabilityimage_1: populated values

Analysis: rows; presence=per_row

Finding f0 · counting unit: rows

populated	8
missing	4
denominator	12
populated fraction	0.6667

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/8 saved source positions [0]

Exceptions: 1/4 saved source positions [5]

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f0')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

availabilityimage_2: populated values

Analysis: rows; presence=per_row

Finding f1 · counting unit: rows

populated	7
missing	5
denominator	12
populated fraction	0.5833

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/7 saved source positions [0]

Exceptions: 1/5 saved source positions [5]

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f1')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

availabilitydose: populated values

Analysis: rows; presence=per_row

Finding f2 · counting unit: rows

populated	4
missing	8
denominator	12
populated fraction	0.3333

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/4 saved source positions [5]

Exceptions: 1/8 saved source positions [0]

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f2')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

availability signaturePresent: image_1, image_2; absent: dose

Analysis: rows; presence=per_row

Finding f3 · counting unit: rows

count	7
denominator	12

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/7 saved source positions [0]

Exceptions: 0/0 saved source positions []

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f3')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

present	image_1, image_2
absent	dose

availability signaturePresent: dose; absent: image_1, image_2

Analysis: rows; presence=per_row

Finding f4 · counting unit: rows

count	4
denominator	12

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/4 saved source positions [5]

Exceptions: 0/0 saved source positions []

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f4')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

present	dose
absent	image_1, image_2

availability signaturePresent: image_1; absent: image_2, dose

Analysis: rows; presence=per_row

Finding f5 · counting unit: rows

count	1
denominator	12

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/1 saved source positions [8]

Exceptions: 0/0 saved source positions []

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f5')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

present	image_1
absent	image_2, dose

similar availabilityimage_1 and image_2 have similar presence

Analysis: rows; presence=per_row

Finding f6 · counting unit: rows

both present	7
either present	8
both absent	4
denominator	12
presence jaccard	0.875
agreement	0.9167

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/7 saved source positions [0]

Exceptions: 1/1 saved source positions [8]

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f6')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

presence implicationimage_1 populated implies image_2 populated

Analysis: rows; presence=per_row

Finding f7 · counting unit: rows

both present	7
either present	8
both absent	4
denominator	12
presence jaccard	0.875
agreement	0.9167
antecedent populated	8
conditional presence	0.875
exception rate	0.125
baseline presence	0.5833

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/7 saved source positions [0]

Exceptions: 1/1 saved source positions [8]

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f7')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

presence implicationimage_2 populated implies image_1 populated

Analysis: rows; presence=per_row

Finding f8 · counting unit: rows

both present	7
either present	8
both absent	4
denominator	12
presence jaccard	0.875
agreement	0.9167
antecedent populated	7
conditional presence	1
exception rate	0
baseline presence	0.6667

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/7 saved source positions [0]

Exceptions: 0/0 saved source positions []

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f8')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

mutually exclusiveimage_1 and dose are mutually exclusive

Analysis: rows; presence=per_row

Finding f9 · counting unit: rows

both present	0
either present	12
both absent	0
denominator	12
presence jaccard	0
agreement	0

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/12 saved source positions [0]

Exceptions: 0/0 saved source positions []

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f9')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

mutually exclusiveimage_2 and dose are mutually exclusive

Analysis: rows; presence=per_row

Finding f10 · counting unit: rows

both present	0
either present	11
both absent	1
denominator	12
presence jaccard	0
agreement	0.08333

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/11 saved source positions [0]

Exceptions: 0/0 saved source positions []

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f10')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

context availabilityimage_1: availability within modality = 'MR' (string)

Analysis: rows; presence=per_row

Finding f11 · counting unit: rows

feature	image_1
populated	8
denominator	8

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/8 saved source positions [0]

Exceptions: 0/0 saved source positions []

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f11')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

context

modality

type	string
value	MR

context availabilityimage_2: availability within modality = 'MR' (string)

Analysis: rows; presence=per_row

Finding f12 · counting unit: rows

feature	image_2
populated	7
denominator	8

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/7 saved source positions [0]

Exceptions: 1/1 saved source positions [8]

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f12')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

context

modality

type	string
value	MR

context availabilitydose: availability within modality = 'MR' (string)

Analysis: rows; presence=per_row

Finding f13 · counting unit: rows

feature	dose
populated	0
denominator	8

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 0/0 saved source positions []

Exceptions: 1/8 saved source positions [0]

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f13')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

context

modality

type	string
value	MR

context summaryAvailability within modality = 'MR' (string)

Analysis: rows; presence=per_row

Finding f14 · counting unit: rows

source rows

denominator

availability

feature image_1
populated 8
denominator 8
feature image_2
populated 7
denominator 8
feature dose
populated 0
denominator 8

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/8 saved source positions [0]

Exceptions: 0/0 saved source positions []

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f14')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

context

modality

type	string
value	MR

context availabilityimage_1: availability within modality = 'CT' (string)

Analysis: rows; presence=per_row

Finding f15 · counting unit: rows

feature	image_1
populated	0
denominator	4

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 0/0 saved source positions []

Exceptions: 1/4 saved source positions [5]

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f15')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

context

modality

type	string
value	CT

context availabilityimage_2: availability within modality = 'CT' (string)

Analysis: rows; presence=per_row

Finding f16 · counting unit: rows

feature	image_2
populated	0
denominator	4

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 0/0 saved source positions []

Exceptions: 1/4 saved source positions [5]

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f16')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

context

modality

type	string
value	CT

context availabilitydose: availability within modality = 'CT' (string)

Analysis: rows; presence=per_row

Finding f17 · counting unit: rows

feature	dose
populated	4
denominator	4

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/4 saved source positions [5]

Exceptions: 0/0 saved source positions []

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f17')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

context

modality

type	string
value	CT

context summaryAvailability within modality = 'CT' (string)

Analysis: rows; presence=per_row

Finding f18 · counting unit: rows

source rows

denominator

availability

feature image_1
populated 0
denominator 4
feature image_2
populated 0
denominator 4
feature dose
populated 4
denominator 4

Representative source rows

Positions are zero-based offsets in the original ordered source, not dataframe index labels. Saved samples are the first matches in source order; their size is not the total support.

Examples: 1/4 saved source positions [5]

Exceptions: 0/0 saved source positions []

In Python, with this result named result and its identical ordered source named df:

result.inspect(df, 'f18')

Use all_matches=True to retrieve all matching rows and exceptions=True for exception rows. These operations require the original source and a Python session.

context

modality

type	string
value	CT