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The use of outcome feedback by emergency medicine physicians: Results of a physician survey

2018· article· en· W2902592658 on OpenAlexafffund
Rakesh Gupta, Isaac Siemens, Sam Campbell

Bibliographic record

VenueWorld Journal of Emergency Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsDalhousie UniversityUniversity of TorontoMcMaster University
FundersDalhousie University
KeywordsMedicineObservational studyEmergency departmentFamily medicineOutcome (game theory)MEDLINENursingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Feedback on patient outcomes is invaluable to the practice of emergency medicine but examples of effective forms of feedback have not been well characterized in the literature. We describe one system of emergency department (ED) outcome feedback called the return visit report (RVR) and present the results of a survey assessing physicians' perceptions of this novel form of feedback. METHODS: An Opinio web-based survey was conducted in 81 emergency physicians (EPs) at three EDs. RESULTS: Of the 81 physicians surveyed, 40 (49%) responded. Most participants indicated that they frequently review their RVRs (83%), that RVRs are valuable to their practice of medicine (80%), and that RVRs alter their practice in future encounters (57%). Respondents reported seeking other forms of outcome feedback including speaking with other EPs (83%) and reviewing discharge summaries of admitted patients (87%). There was no correlation between demographic data and use of RVRs. CONCLUSION: EPs value RVRs as a form of feedback. RVRs could be improved by reducing the observational interval and optimizing report relevance and differential weighting.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.096
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.096
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.150
GPT teacher head0.413
Teacher spread0.263 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations9
Published2018
Admission routes2
Has abstractyes

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