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Statistical Techniques in General Surgery Literature: What Do We Need to Know?

2018· review· en· W2885963568 on OpenAlexaff
Phillip J. Williams, Patrick Murphy, Julie Ann M. Van Koughnett, Michael Ott, Luc Dubois, Laura Allen, Kelly Vogt

Bibliographic record

VenueJournal of the American College of Surgeons · 2018
Typereview
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineNeed to knowGeneral surgery

Abstract

fetched live from OpenAlex

Williams, Phillip J. MD; Murphy, Patrick MD, MSc, FRCSC; Van Koughnett, Julie Ann M. MD, MEd, FRCSC, FACS; Ott, Michael C. MD, MEd, FRCSC; Dubois, Luc MD, MSc, FRCSC; Allen, Laura MSc; Vogt, Kelly N. MD, MSc, FRCSC Author Information

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)0.048
metaresearch head score (Gemma)0.204
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.204
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0140.019
Science and technology studies0.0010.007
Scholarly communication0.0070.016
Open science0.0040.003
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0090.003

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 head0.083
GPT teacher head0.432
Teacher spread0.349 · 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
DomainMethods
GenreReview

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

Citations5
Published2018
Admission routes1
Has abstractyes

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