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Record W2927981269 · doi:10.1016/j.kint.2019.03.015

An international Delphi survey helped develop consensus-based core outcome domains for trials in peritoneal dialysis

2019· article· en· W2927981269 on OpenAlexaff
Karine Manera, Allison Tong, Jonathan C. Craig, Jenny I. Shen, Shilpanjali Jesudason, Yeoungjee Cho, Bénédicte Sautenet, Armando Teixeira‐Pinto, Martin Howell, Angela Yee‐Moon Wang, Edwina A. Brown, Gillian Brunier, Jeffrey Perl, Jie Dong, Martin Wilkie, Rajnish Mehrotra, Roberto Pecoits‐Filho, Saraladevi Naicker, Tony Dunning, Nicole Scholes‐Robertson, David W. Johnson

Bibliographic record

VenueKidney International · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of TorontoSt. Michael's HospitalHealth Sciences CentreSunnybrook Health Science Centre
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesInternational Society for Peritoneal Dialysis
KeywordsPeritoneal dialysisMedicineConsensus conferenceOutcome (game theory)Delphi methodCore (optical fiber)Intensive care medicineFamily medicineInternal medicineComputer science

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)0.328
metaresearch head score (Gemma)0.355
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.672
Threshold uncertainty score0.828

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3280.355
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0090.005
Science and technology studies0.0050.004
Scholarly communication0.0040.006
Open science0.0020.011
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0110.002

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 head0.378
GPT teacher head0.548
Teacher spread0.170 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
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

Citations104
Published2019
Admission routes1
Has abstractno

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