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Record W4232091657 · doi:10.1038/s41374-021-00571-z

Abstracts from USCAP 2021: Quality and Patient Safety (953-989)

2021· article· en· W4232091657 on OpenAlexaff
Jason Chair, Rhonda K. Yantiss, Kristin Jensen Chair, Cme Subcommittee, Laura C. Collins, David Kaminsky, Zubair Baloch, Daniel J. Brat, Sarah Dry, William C. Faquin, Yuri Fedoriw, Karen Fritchie, Jennifer Gordetsky, Melinda Lerwill, Anna Marie Mulligan, Liron Pantanowitz, David Papke, Carlos Parra‐Herran, Rajiv Patel, Deepa T. Patil, Charles M. Quick, Lynette M. Sholl, Olga K. Weinberg, Maria Westerhoff, Michael Cho, Andy Ai, Keith Sweeney, Hui Chen, Lei Huo, Yun Wu, Erika Resetkova, Esther Yoon, Billy Wang, Constance T. Albarracin, Ethar Al‐Husseinawi, Rashna Madan, Fang Fan, Mohamed Alhamar, Gaurav Sharma, Caroline Maurer, Justin Caron, Ronald Paler, Brian Theisen, Jeremy Hart, Vipul A. Trivedi, Joe C. Rutledge, Lamé Balikani, Sharon Copley, Kossivi Dantey, Edward C. Lynch, Olukemi Esan, Michael Bonert, Charles Jian, Ipshita Kak, Gary Foster, Pierre Major, Giovanni Centonze, Patrick Maisonneuve, Natalie Prinzi, Laura Cattaneo, Giovanna Sabella, Vincenzo Lagano, Eleonora Pisa, Alessandro Mangogna, Mauro Roberto Benvenuti, Marco Volante, Aldo Scarpa, Carlo Capella, Guido Rindi, Massimo Milione, Kenneth Emancipator

Bibliographic record

VenueLaboratory Investigation · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsQuality (philosophy)MedicinePatient safetyMedical emergencyPolitical scienceHealth careLaw

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)0.006
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.625
Threshold uncertainty score0.535

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.003
Science and technology studies0.0020.001
Scholarly communication0.0070.001
Open science0.0020.003
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.6250.386

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 head0.022
GPT teacher head0.272
Teacher spread0.250 · 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
Domainnot available
GenreOther

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

Citations1
Published2021
Admission routes1
Has abstractno

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