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Record W4403922314 · doi:10.4103/cmrp.cmrp_110_24

Beyond boundaries: An exploration of general surgery education in Canada and India

2024· article· en· W4403922314 on OpenAlexaffabout
Prachikumari Patel, Samrat Ray, Luckshi Rajendran, Hala Muaddi, Taylor M. Coe, Ahmer Irfan, Chaya Shwaartz

Bibliographic record

VenueCurrent Medicine Research and Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsGeology

Abstract

fetched live from OpenAlex

ABSTRACT General surgery is a dynamic and multifaceted field influenced by diverse factors, ranging from cultural norms to healthcare system structures and technological advancements. This review paper delves into a comparative exploration of the educational landscapes in Canada and India, unravelling the intricacies of training aspiring general surgeons. As two nations with distinct healthcare systems and educational frameworks, Canada and India offer unique perspectives on the journey from medical school to independent surgical practice. This article compares the residency application process, training structure, assessment methods, challenges faced during clinical training and the role of research in surgical programmes between the two nations. Through this comparative lens, we seek to provide valuable insights that may inform future developments in surgical education, fostering a global exchange of knowledge and practices to advance healthcare systems worldwide.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.905
Threshold uncertainty score0.689

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.018
Science and technology studies0.0130.007
Scholarly communication0.0080.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.162
GPT teacher head0.509
Teacher spread0.347 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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