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Improving Cancer Diagnosis and Care: Patient Access to High-Quality Oncologic Pathology

2019· article· en· W2965982329 on OpenAlexfundno aff
Sharyl J. Nass, Michael B. Cohen, Ritu Nayar, Mary M. Zutter, Erin P. Balogh, Richard L. Schilsky, Hedvig Hricak, Kojo S.J. Elenitoba‐Johnson

Bibliographic record

VenueThe Oncologist · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsnot available
FundersNational Cancer InstituteCenters for Disease Control and PreventionNational Comprehensive Cancer NetworkAmerican Association for Cancer ResearchBristol-Myers SquibbAmerican Society of Clinical OncologyAmerican College of RadiologyU.S. Food and Drug AdministrationMemorial Sloan-Kettering Cancer CenterFlatiron HealthOncology Nursing SocietyMerckHelsinn TherapeuticsPfizerCancer Research SocietyAmerican Cancer SocietyLivestrong Foundation
KeywordsMedicineCancerPatient careMedical physicsPathologyIntensive care medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

Drawing on discussions at a workshop hosted by the National Cancer Policy Forum, current challenges in pathology are reviewed and practical steps to facilitate high‐quality cancer diagnosis and care through improved patient access to expertise in oncologic pathology are highlighted.

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)0.000
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 score0.195
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.315
Teacher spread0.253 · 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

Citations29
Published2019
Admission routes1
Has abstractyes

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