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Record W3015202144 · doi:10.1016/j.jtcvs.2020.03.104

Commentary: Call for teamwork to be a class I, evidence-level A recommendation in all guidelines

2020· letter· en· W3015202144 on OpenAlexaff
Ourania Preventza, Jessica G.Y. Luc

Bibliographic record

VenueJournal of Thoracic and Cardiovascular Surgery · 2020
Typeletter
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineTeamworkClass (philosophy)MEDLINEMedical educationArtificial intelligenceManagement

Abstract

fetched live from OpenAlex

believed they were effective.In demonstrating the variability across centers performing complex aortic surgery, the study highlights areas in need of more research or best practice recommendations.The notable limitations of the study, which the authors thoroughly address, relate primarily to the inherent issues with bias in survey analysis.Self-reporting may introduce issues due to respondents providing answers that they believe conform with a perceived norm rather than their actual practice patterns.Recent cases or complications may result in recall bias, with respondents providing answers that reflect their recent experience rather than their overall true practice pattern.Despite these limitations, the authors provide valuable insight into current real-world practices in the management of complications in patients undergoing complex thoracic aortic surgery.

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)0.018
metaresearch head score (Gemma)0.173
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.122
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.173
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0030.003
Science and technology studies0.0070.006
Scholarly communication0.0090.006
Open science0.0050.004
Research integrity0.1220.086
Insufficient payload (model declined to judge)0.0170.019

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 head0.232
GPT teacher head0.394
Teacher spread0.162 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2020
Admission routes1
Has abstractno

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