MétaCan
Menu
Back to cohort

Participating in the Peer Review Process: The Journal of Cardiac Failure Construct

2022· editorial· en· W4311932199 on OpenAlexaff
Emer Joyce, Colleen K McIllvennan, Jill Howie‐Esquivel, Andrew J. Sauer, Ashish Correa, Vanessa Blumer, Quentin R. Youmans, Jesús Álvarez‐García, Helena R. Chang, Jessica Overbey, Elena Deych, Shashank S. Sinha, Alanna A. Morris, Ersilia M. DeFilippis, Nosheen Reza, Jillianne Code, Alexander Hajduczok, Marat Fudim, Brett Rollins, Justin Vader, Ileana L. Piña, Jeffrey J. Teuteberg, Shelley Zieroth, Randall C. Starling, Martha Gulati, Robert J. Mentz, Anuradha Lala

Bibliographic record

VenueJournal of Cardiac Failure · 2022
Typeeditorial
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversity of ManitobaUniversity of British Columbia
Fundersnot available
KeywordsMedicineConstruct (python library)Process (computing)Computer networkProgramming language

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)0.041
metaresearch head score (Gemma)0.125
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.959
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.125
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0070.003
Bibliometrics0.0050.002
Science and technology studies0.0080.007
Scholarly communication0.0220.008
Open science0.0060.003
Research integrity0.0350.039
Insufficient payload (model declined to judge)0.0110.006

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 head0.015
GPT teacher head0.311
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.

Study designNot applicable
DomainEvaluation
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

Citations2
Published2022
Admission routes1
Has abstractno

Explore more

Same venueJournal of Cardiac FailureSame topicCardiac, Anesthesia and Surgical OutcomesFrench-language works237,207