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Record W4393111972 · doi:10.1016/j.bja.2024.02.021

International consensus is needed on a core outcome set to advance the evidence of best practice in cancer prehabilitation services and research

2024· editorial· en· W4393111972 on OpenAlexaff
Anna Myers, Rachael Barlow, Gabriele Baldini, Anna Campbell, Franco Carli, Esther Carr, T. Collyer, Gerard Danjoux, June Davis, Linda Denehy, J. W. Durrand, Chelsia Gillis, David Greenfield, Stuart P. Griffiths, Michael P. W. Grocott, Liam Humphreys, Sandy Jack, Carol Keen, Denny Levett, Zoe Merchant, John Moore, Susan Moug, William Ricketts, Daniel Santa Mina, John Saxton, Clare Shaw, Garry A. Tew, Michael Thelwell, Malcolm West, Robert Copeland

Bibliographic record

VenueBritish Journal of Anaesthesia · 2024
Typeeditorial
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of TorontoUniversity Health NetworkMcGill UniversityRoyal Victoria HospitalRoyal Victoria Regional Health Centre
Fundersnot available
KeywordsPrehabilitationCore (optical fiber)Set (abstract data type)Outcome (game theory)Evidence-based practiceMedicineComputer scienceAlternative medicinePhysical therapyEconomics

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)0.157
metaresearch head score (Gemma)0.380
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.843
Threshold uncertainty score0.832

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1570.380
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0130.006
Bibliometrics0.0100.006
Science and technology studies0.0060.009
Scholarly communication0.0200.014
Open science0.0070.004
Research integrity0.0280.047
Insufficient payload (model declined to judge)0.0120.008

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 head0.183
GPT teacher head0.551
Teacher spread0.368 · 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 designTheoretical or conceptual
DomainMethods
GenreEditorial

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

Citations8
Published2024
Admission routes1
Has abstractno

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