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Record W2612384326 · doi:10.22374/cjgim.v12i1.206

Tips for Facilitating Morning Report

2017· article· en· W2612384326 on OpenAlexaffvenueabout
Luke Devine, Wayne L. Gold, Andrea Page, Steven L. Shumak, Brian M. Wong, Natalie Wong, Lynfa Stroud

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of TorontoSt. Michael's HospitalSunnybrook Health Science CentreMount Sinai Hospital
Fundersnot available
KeywordsMedicineMorningInternal medicine

Abstract

fetched live from OpenAlex

Morning report (MR) is a valued educational experience in internal medicine training programs. Many senior residents and faculty have not received formal training in how to effectively facilitate MR. Faculty at the University of Toronto were surveyed to provide insights into what they felt were key elements for the successful facilitation of MR. These insights fell within 5 major categories: planning and preparation, the case, running the show, wrapping up and closing the loop.

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)0.027
metaresearch head score (Gemma)0.114
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: Methods · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.114
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0050.003
Scholarly communication0.0070.012
Open science0.0030.009
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0520.035

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 head0.057
GPT teacher head0.390
Teacher spread0.333 · 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
GenreMethods

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
Published2017
Admission routes3
Has abstractyes

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