MétaCan
Menu
Back to cohort
Record W2330899390 · doi:10.2106/jbjs.n.00739

National and International Postmarket Research and Surveillance Implementation

2014· article· en· W2330899390 on OpenAlexfundno aff
Art Sedrakyan, Elizabeth W. Paxton, Stephen E. Graves, Rebecca Love, Danica Marinac‐Dabic

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
FundersWeill Cornell Medical CollegeU.S. Public Health ServiceU.S. Food and Drug AdministrationHamilton Health Sciences FoundationKaiser Permanente
KeywordsArt historyArt

Abstract

fetched live from OpenAlex

Sedrakyan, Art MD, PhD; Paxton, Elizabeth MA; Graves, Stephen MBBS, DPhil, FRACS, FAOrthA; Love, Rebecca MPH, RN; Marinac-Dabic, Danica MD, PhD 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)0.115
metaresearch head score (Gemma)0.187
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.115
Threshold uncertainty score0.608

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1150.187
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.005
Science and technology studies0.0030.002
Scholarly communication0.0070.007
Open science0.0040.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0580.004

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 head0.065
GPT teacher head0.362
Teacher spread0.296 · 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.

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

Citations53
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Bone and Joint SurgerySame topicTotal Knee Arthroplasty OutcomesFrench-language works237,207