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Record W2140966604 · doi:10.1002/jso.21282

Standardized synoptic cancer pathology reporting: A population‐based approach

2009· article· en· W2140966604 on OpenAlexaffabout
John R. Srigley, Tom McGowan, Andrea MacLean, Marilyn Raby, Jillian Ross, Sarah Krämer, Carol Sawka

Bibliographic record

VenueJournal of Surgical Oncology · 2009
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsUniversity of TorontoMcMaster UniversityCancer Care Ontario
Fundersnot available
KeywordsMedicineCancerPopulationPathologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Cancer pathology reports contain information which is critical for patient management and for cancer surveillance, resource planning, and quality purposes. The College of American Pathologists (CAP) has defined scientifically validated content of checklists that form the basis for synoptic cancer pathology reporting. We outline how the CAP standards were implemented in a large Canadian province over a 3-year period resulting in improvements in rates of synoptic reporting and completeness of cancer pathology reporting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.098
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score0.520

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0980.097
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0230.014
Science and technology studies0.0020.003
Scholarly communication0.0050.005
Open science0.0050.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.394
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainReporting
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations204
Published2009
Admission routes2
Has abstractyes

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