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Record W3047675162 · doi:10.1136/jclinpath-2020-206948

Borderline Gleason scores: communication is the key

2020· article· en· W3047675162 on OpenAlexaff
Murali Varma, Brett Delahunt, Theodorus van der Kwast, Sean R. Williamson, Daniel M. Berney

Bibliographic record

VenueJournal of Clinical Pathology · 2020
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersBarts Charity
KeywordsKey (lock)MedicineBioinformaticsComputer scienceBiologyComputer security

Abstract

fetched live from OpenAlex

The biopsy Gleason score (GS) is a critical component of patient management having been demonstrated to be an excellent predictor of patient outcome.1 2 While the application of Gleason grading is generally straightforward, grading is subject to significant interobserver variation3 and divergent opinions may confuse clinicians and patients. Interobserver variation may be due to the application of different grading rules or more commonly different interpretations of borderline morphological appearances. The former is avoidable and multiple consensus conferences have sought to define uniform criteria for grading prostate cancer.4–7 However, the latter is inevitable in a morphological continuum. We seek to explain why precise grading becomes less important in this scenario, if the findings are effectively communicated by the pathologist and correctly interpreted by the clinician. Gleason grades commonly represent a morphological continuum from well-formed glands (pattern 3) to increasingly smaller-sized and poorly formed glandular proliferations (pattern 4) and finally to almost no glandular differentiation (pattern 5). Thus, GS is often a continuous variable with arbitrary cut-offs. This is analogous to serum Prostate Specific Antigen (PSA) where arbitrary cut-offs are used to categorise patients into risk groups. However, unlike serum PSA, grade is reported as …

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)0.001
metaresearch head score (Gemma)0.002
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 score0.273
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.119
GPT teacher head0.429
Teacher spread0.310 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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