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Record W4214750782 · doi:10.1093/bjsopen/zrac038

Author response to: Global surgery education in Europe: a landscape analysis

2022· letter· en· W4214750782 on OpenAlexaff
Lotta Velin, Adriana C. Panayi, Iris Lebbe, Emmanuelle M. Koehl, Gauthier Willemse, Dominique Vervoort

Bibliographic record

VenueBJS Open · 2022
Typeletter
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Dear Editor We read the comment from Dr Bandyopadhyay, questioning our findings that show only 16 global surgery centres in Europe, with great interest1. We thank the author for providing us with an opportunity to expand on the limitations of our analysis and acknowledge that more institutions, such as those presented by the author, are involved in surgical care delivery in variable-resource contexts. Our analysis focused exclusively on academic global surgery initiatives and educational programmes directly associated with medical schools. Although non-governmental organizations such as KidsOR (founded in Scotland) and the Global Surgery Foundation (located in Switzerland), and student initiatives such as InciSioN chapters, were not captured by our analysis, they are central to the European global surgery landscape. Similarly, although not hosting traditional institution-based learning, subregional initiatives such as the Nordic Network for Global Surgery and Anesthesia in the Nordic countries and the German Society for Global Surgery in Germany facilitate research and educational collaborations between institutions and individuals. We excluded non-academic initiatives for multiple reasons. First, many informal ad hoc opportunities, such as student chapters or projects based on personal partnerships, may not be described online, and were excluded to ensure consistency. Second, most hospitals have some individuals working clinically abroad (e.g. annual ‘missions’) with some hospitals even having long-term relationships established. These initiatives, however, are rarely established at the university level, as reflected in our analysis. Third, trainees often have limited opportunities to meaningfully participate in clinical initiatives abroad which are not based on institutional collaborations. Many, albeit certainly not all, of these fly-in ‘missions’ are marked by power asymmetries contradicting the spirit of equity emphasized in the definition of global surgery. Overall, our focus on academic initiatives sought to serve as a proxy for visibility and accessibility for trainees to start to engage as future global surgery leaders.

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)0.004
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.052
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0060.003
Open science0.0020.002
Research integrity0.0520.042
Insufficient payload (model declined to judge)0.0110.005

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 head0.043
GPT teacher head0.387
Teacher spread0.344 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2022
Admission routes1
Has abstractno

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