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Record W2017674388 · doi:10.1017/s0317167100004467

How Well Does Neurology Residency Mirror Practice?

2005· article· en· W2017674388 on OpenAlexaffvenue
Fraser Moore, Colin Chalk

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeurology and Historical Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsNeurologyResidency trainingMedical diagnosisMedicineClinical PracticeFamily medicinePediatricsPsychiatryMedical educationContinuing educationPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To report the residency training experience of two neurologists, focusing on setting and diagnoses encountered. METHODS: Data from prospective patient logs kept by the authors during residencies completed a decade apart were compared with each other and the literature. RESULTS: The range of diagnoses was broadly similar between residencies, as were the proportions of common or uncommon disorders, and the proportion of cases without a neurological diagnosis. Although most patients were seen in in-patient settings, the rates at which common neurological conditions and functional disorders were seen was comparable to published experiences of community neurologists. CONCLUSIONS: The diagnostic profile of North American neurology residency appears to be relatively stable, regardless of location or date of training. In several respects, the content of current neurology residencies mirrors clinical practice well. Changes to residency training are doubtless needed, but they should be guided by a clear understanding of the experiences of contemporary trainees.

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)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0070.014
Scholarly communication0.0010.003
Open science0.0040.000
Research integrity0.0000.003
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 head0.048
GPT teacher head0.284
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations5
Published2005
Admission routes2
Has abstractyes

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