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Record W4412455885 · doi:10.1016/s0140-6736(25)00985-7

Surgical health policy 2025–35: strengthening essential services for tomorrow's needs

2025· review· en· W4412455885 on OpenAlexaff
Dmitri Nepogodiev, Maria Picciochi, Adesoji Ademuyiwa, Adewale Adisa, Anita Eseenam Agbeko, Maria-Lorena Aguilera, Fareeda Agyei, Philip Alexander, Jaymie Henry, Theophilus Teddy Kojo Anyomih, Alazar Berhe Aregawi, Rifat Atun, Bruce Biccard, Mumba Chalwe, Kathryn Chu, Arri Coomarasamy, Richard Crawford, Ara Darzi, Justine Davies, Zipporah Gathuya, Christina George, Abdul Ghaffar, Dhruva Ghosh, James Glasbey, Parvez Haque, Ewen M. Harrison, Afua A. J. Hesse, JC Allen Ingabire, Sivesh K. Kamarajah, Claire Karekezi, Deirdré Kruger, Marie Carmela Lapitan, Asad Latif, Ismaïl Lawani, Virginia Ledda, Elizabeth Li, Cortland Linder, Emmanuel Makasa, Janet Martin, Salome Maswime, Sonia Mathai, John G. Meara, Fortunate Mudede-Moffat, Faustin Ntirenganya, Kee B. Park, L Phelan, C.S. Pramesh, Antonio Ramos‐De la Medina, Nakul Raykar, Robert Rivello, April Camilla Roslani, Nobhojit Roy, Lubna Samad, Mark G. Shrime, Soha Sobhy, Richard Sullivan, Stephen Tabiri, Viliami Tangi, Elizabeth Tissingh, Thomas G. Weiser, Omolara Williams, Aneel Bhangu

Bibliographic record

VenueThe Lancet · 2025
Typereview
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsWestern UniversityCentre for Global Health Research
FundersNational Institute for Health and Care Research
KeywordsBusinessPolicy developmentNursingMedicineEconomic policy

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.008
metaresearch head score (Gemma)0.015
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: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0220.004

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 head0.102
GPT teacher head0.525
Teacher spread0.423 · 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
GenreReview

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

Citations27
Published2025
Admission routes1
Has abstractno

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