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Record W2912088129 · doi:10.1016/s0140-6736(18)33139-8

Global burden of postoperative death

2019· letter· en· W2912088129 on OpenAlexaff
Dmitri Nepogodiev, Janet Martin, Bruce Biccard, Alex Makupe, Aneel Bhangu, Adesoji Ademuyiwa, Adewale Adisa, Maria-Lorena Aguilera, Sohini Chakrabortee, J.E.F. Fitzgerald, Dhruva Ghosh, James Glasbey, Ewen M. Harrison, JC Allen Ingabire, Hosni Salem, Marie Carmela Lapitan, Ismaïl Lawani, D. Lissauer, Laura Magill, Rachel Moore, Daniel Osei‐Bordom, Thomas Pinkney, Ahmad Uzair Qureshi, Antonio Ramos‐De la Medina, Sarah Rayne, Sudha Sundar, Stephen Tabiri, Azmina Verjee, Raul Yepez, O. James Garden, Richard Lilford, Peter Brocklehurst, Dion Morton

Bibliographic record

VenueThe Lancet · 2019
Typeletter
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsWestern University
FundersDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsMedicineHigh income countriesMortality rateGlobal healthScopusCommissionWelfareHealth careHealthcare systemDeveloping countryMEDLINESurgeryEconomic growthPublic healthBusinessPolitical scienceEconomicsNursing

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)0.004
metaresearch head score (Gemma)0.020
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 score0.030
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0300.025
Insufficient payload (model declined to judge)0.0130.009

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 head0.039
GPT teacher head0.324
Teacher spread0.285 · 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

Citations610
Published2019
Admission routes1
Has abstractno

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