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Record W2552396818 · doi:10.1111/anae.13709

An evaluation of inpatient morbidity and critical care provision in Zambia

2016· article· en· W2552396818 on OpenAlexaff
P. J. Dart, John Kinnear, M. Dylan Bould, S Mwansa, Z. Rakhda, David Snell

Bibliographic record

VenueAnaesthesia · 2016
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineReferralTertiary referral hospitalEmergency medicineEarly warning scoreIntensive care unitCritical care nursingTertiary careMedical careHuman immunodeficiency virus (HIV)Family medicinePediatricsMedical emergencyIntensive care medicineHealth careRetrospective cohort studySurgery

Abstract

fetched live from OpenAlex

The aim of this study was to objectively measure demand for critical care services in a southern African tertiary referral centre. We carried out a point prevalence study of medical and surgical admissions over a 48-h period at the University Teaching Hospital, Lusaka, recording the following: age; sex; diagnosis; Human Immunodeficiency Virus (HIV) status and National Early Warning Score. One-hundred and twenty medical and surgical admissions were studied. Fifty-four patients (45%) had objective evidence of a requirement for critical care review and potential or probable admission to an intensive care unit, according to the Royal College of Physicians (UK) guidelines. A greater than expected HIV rate was also noted; 53 of 75 tested patients (71%). When applied to the estimated 17,496 annual acute admissions, this would equate to 7873 patients requiring critical care input annually at this hospital alone. In contrast to this demand, we identified 109 critical care beds nationally, and only eight at this institution.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.362
Teacher spread0.321 · 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

Citations38
Published2016
Admission routes1
Has abstractyes

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