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Record W2006372415 · doi:10.1136/jclinpath-2014-202370

Web based pathology assessment in RTOG 98-04

2014· article· en· W2006372415 on OpenAlexaff
Wendy A. Woodward, Nour Sneige, Kathryn Winter, Henry M. Kuerer, Clifford A. Hudis, Eileen Rakovitch, Barbara L. Smith, Lori J. Pierce, Isabelle M. Germano, Anthony T. Pu, Eleanor Walker, David Lawrence Grisell, Julia White, Beryl McCormick

Bibliographic record

VenueJournal of Clinical Pathology · 2014
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Cancer Institute
KeywordsMedicineGrading (engineering)ConcordanceSurgical pathologyDuctal carcinomaPathologicalPathologyRadiation therapyMedical physicsRadiologyBreast cancerInternal medicineCancer

Abstract

fetched live from OpenAlex

AIMS: Radiation Therapy Oncology Group 98-04 sought to identify women with ‘good risk’ ductal carcinoma in situ (DCIS) who receive no significant benefit from radiation. Enrolment criteria excluded close or positive margins and grade 3 disease. To ensure reproducibility in identifying good risk pathology, an optional web based teaching tool was developed and a random sampling of 10% of submitted slides were reviewed by a central pathologist. METHODS: Submitting pathologists were asked to use the web based teaching tool and submit an assessment of the tool along with the pathology specimen form and DCIS H&E stained slide. Per protocol pathology was centrally reviewed for 10% of the cases. RESULTS: Of the 55 DCIS cases reviewed, three had close or positive margins and three were assessed to include grade 3 DCIS, therefore 95% of DCIS cases reviewed were correctly graded, and 89% reviewed were pathologically appropriate for enrolment. Regarding the teaching tool, 13% of DCIS cases included forms that indicated the website was used. One of these seven who used the website submitted DCIS of grade 3. CONCLUSIONS: Central review demonstrates high pathological concordance with enrolment eligibility, particularly with regard to accurate grading. The teaching tool appeared to be underused.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.509
Teacher spread0.460 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
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

Citations6
Published2014
Admission routes1
Has abstractyes

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