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Record W3159869422 · doi:10.1002/aet2.10607

The standardized letter of evaluation in emergency medicine: Are the qualifications useful?

2021· article· en· W3159869422 on OpenAlexaff
Danielle T. Miller, Sara Krzyzaniak, Alexandra Mannix, Al’ai Alvarez, Teresa M. Chan, Dayle Davenport, Daniel Eraso, Clary J. Foote, Katarzyna Gore, Melissa Parsons, Michael Gottlieb

Bibliographic record

VenueAEM Education and Training · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRanking (information retrieval)Rank correlationRank (graph theory)Position (finance)PsychologySpearman's rank correlation coefficientPairwise comparisonMedical educationMetric (unit)CorrelationMedicineFamily medicineStatisticsMathematicsOperations managementComputer scienceInformation retrievalEngineeringBusiness

Abstract

fetched live from OpenAlex

OBJECTIVES: The standardized letter of evaluation (SLOE) in emergency medicine (EM) is a widely used metric for determining interview invitations and ranking of candidates. Previous research has questioned the validity of certain sections of the SLOE. However, there remains a paucity of literature on the qualifications for EM section, which evaluates seven attributes of applicants. The aim of this study was to determine the correlation between the qualifications questions and grades, global assessment, and anticipated rank list position for EM applicants. METHODS: A multi-institutional cross-sectional study was performed using SLOEs from applicants to three geographically distinct U.S. EM residency programs during the 2019-2020 application cycle. We abstracted EM rotation grade, qualifications scores, global assessment, and anticipated rank list position from the SLOEs. A Spearman correlation was calculated between each of the qualifications scores and the applicant's grades, global assessment, and anticipated rank list position in a pairwise fashion. RESULTS: In total, 2,106 unique applicants (4,939 SLOEs) were included. Of the seven qualifications for EM questions, three were moderately to strongly correlated with global assessment and anticipated rank list position: "ability to develop and justify an appropriate differential and a cohesive treatment plan" (ρ = 0.65 and ρ = 0.63, respectively; p < 0.001), "how much guidance do you predict this applicant will need during residency?" (ρ = 0.68 and ρ = 0.68, respectively; p < 0.001), and "what is your prediction of success for the applicant?" (ρ = 0.69 and ρ = 0.69, respectively; p < 0.001). There was no strong correlation between the seven qualifications and grades. CONCLUSIONS: There was a moderate to strong correlation between three of seven qualifications for EM questions (ability to develop and justify a differential and develop a cohesive plan, anticipated need for the amount of guidance, and prediction of success) with both global assessment and anticipated rank list position, suggesting that these qualifications may provide the most useful data to residency selection while some of the other factors may not be needed.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.132
GPT teacher head0.413
Teacher spread0.281 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2021
Admission routes1
Has abstractyes

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