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Record W4383746602 · doi:10.1016/s2542-5196(23)00124-9

Environmentally sustainable surgical health systems: an analysis of policies, tools, and guidelines

2023· article· en· W4383746602 on OpenAlexaff
Anisa Nazir, Xiya Ma, Dominique Vervoort

Bibliographic record

VenueThe Lancet Planetary Health · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of TorontoUniversité de MontréalSt. Michael's Hospital
Fundersnot available
KeywordsCarbon footprintScopusMedicineHealth careClimate changeMEDLINEBusinessMedical emergencyPolitical scienceGreenhouse gas

Abstract

fetched live from OpenAlex

Climate change and insufficient access to surgical care are two intersecting global health challenges that disproportionately affect populations in areas, such as low-income and middle-income countries (LMICs), rural and remote communities, and island states. Five billion people worldwide have no access to safe surgical care and anaesthesia, resulting in over 17 million preventable deaths per year.1 In 2015, The Lancet Commission on Global Surgery recommended scaling up surgical, anaesthesia, and obstetrics care through National Surgical, Obstetric, and Anaesthesia Plans (NSOAPs), as a strategic effort embedded within countries’ national health plans.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.141
GPT teacher head0.376
Teacher spread0.234 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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