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Record W2390524166 · doi:10.1016/s2214-109x(16)30082-1

Tracking perioperative mortality and maternal mortality: challenges and opportunities

2016· article· en· W2390524166 on OpenAlexaff
Sumedha Arya, Joshua S Ng-Kamstra, John G. Meara, Ana Langer

Bibliographic record

VenueThe Lancet Global Health · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPerioperativeMEDLINEMedicineIntensive care medicinePolitical scienceSurgery

Abstract

fetched live from OpenAlex

Access to surgery remains inequitable worldwide, with 5 billion people lacking safe and affordable surgical and anaesthesia care when needed.1 The Lancet Commission on Global Surgery was convened in 2013 to assess the state of surgery around the world, provide recommendations for improving access, and propose indicators for assessing national surgical systems. A key safety indicator is the perioperative mortality rate (POMR). This is defined by the Commission as the number of all-cause deaths before discharge in patients who have undergone a procedure in an operating theatre, divided by the total number of procedures, and presented as a percentage.

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)0.054
metaresearch head score (Gemma)0.198
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.054
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.198
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0080.010
Science and technology studies0.0010.001
Scholarly communication0.0050.007
Open science0.0030.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.002

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 head0.241
GPT teacher head0.417
Teacher spread0.176 · 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 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

Citations4
Published2016
Admission routes1
Has abstractyes

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