MétaCan
Menu
Back to cohort
Record W4404043028 · doi:10.1111/ans.19298

A comparison of orthopaedic surgery training across five English‐speaking countries

2024· article· en· W4404043028 on OpenAlexaffabout
Alexander Boyle, Corey D. Chan, Alice Liu, David N. Bernstein, Ian Incoll

Bibliographic record

VenueANZ Journal of Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of British Columbia
FundersNational Institute for Health and Care Research
KeywordsMedicineGraduation (instrument)Training (meteorology)SpecialtyOrthopedic surgeryCompetition (biology)Selection (genetic algorithm)Medical educationFamily medicineSurgeryGeography

Abstract

fetched live from OpenAlex

INTRODUCTION: There is undocumented and unjustified variability in orthopaedic surgery training between countries. This study compares and contrasts the main features of orthopaedic training in Australia, New Zealand, the United Kingdom, United States, and Canada. METHODS: Comparisons included: competition for, and selection into, training; training pathway structures; training requirements, and; training length. RESULTS: Selection into orthopaedic surgery training is competitive in all countries assessed with acceptance rates ranging from 22%-26% in Australia and New Zealand to 85% in Canada. Minimum length of post-medical school training varies from 5 years in the USA and Canada, to 8 years in Australia, 9 years in New Zealand, and 10 years in the United Kingdom. All countries encourage participation in research during training, although there are varying requirements. Significant bottlenecks characterize selection into training in Australia, New Zealand, and the United Kingdom, meaning the majority of doctors take more than a decade from medical school graduation to obtaining their specialty surgery qualification. CONCLUSIONS: There is high variability between the orthopaedic training programs of the studied countries. An awareness of these differences and similarities may help improve training, or provide solutions for identified gaps in each country.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.123
GPT teacher head0.385
Teacher spread0.262 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueANZ Journal of SurgerySame topicSurgical Simulation and TrainingFrench-language works237,207