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Record W3024533433 · doi:10.1016/j.humpath.2020.04.012

Data set for the reporting of pheochromocytoma and paraganglioma: explanations and recommendations of the guidelines from the International Collaboration on Cancer Reporting

2020· review· en· W3024533433 on OpenAlexaff
Lester D.�R. Thompson, Anthony J. Gill, L. Sylvia, Roderick Clifton‐Bligh, Ronald R. de Krijger, Noriko Kimura, Paul Komminoth, Ernest E. Lack, Jacques W.M. Lenders, Ricardo V. Lloyd, Thomas Papathomas, Peter M. Sadow, Arthur S. Tischler

Bibliographic record

VenueHuman Pathology · 2020
Typereview
Languageen
FieldMedicine
TopicAdrenal and Paraganglionic Tumors
Canadian institutionsUniversity Health Network
FundersNational Cancer Institute
KeywordsParagangliomaMedicineStandardizationContext (archaeology)BiobankEpidemiologyPheochromocytomaRisk stratificationPathologyComputer scienceInternal medicineBioinformaticsGeography

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.077
metaresearch head score (Gemma)0.145
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.145
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0120.015
Bibliometrics0.0230.020
Science and technology studies0.0020.003
Scholarly communication0.0070.005
Open science0.0120.006
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0130.007

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 head0.449
GPT teacher head0.517
Teacher spread0.068 · 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 designNot applicable
DomainReporting
GenreMethods

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

Citations44
Published2020
Admission routes1
Has abstractno

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