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Record W90531962 · doi:10.1186/cc12427

Variation in acute care burden and supply across diverse urban settings

2013· article· en· W90531962 on OpenAlexaff
Srinivas Murthy, Shamly Austin, Hannah Wunsch, NK Adhikari, Veena Karir, Kathy Rowan, ST Jacob, Jorge I. Salluh, Fernando A. Bozza, Bin Du, Youzhong An, BongHee Lee, Felicia Wu, Chris Oppong, Ramesh Venkataraman, Vimalraj Velayutham, Derek C. Angus

Bibliographic record

VenueCritical Care · 2013
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsMedicineAcute careMedical emergencyVariation (astronomy)PopulationHealth careEnvironmental healthEconomic growth

Abstract

fetched live from OpenAlex

The World Bank has warned that the rapid growth of the world's urban population can only be accommodated safely if cities adequately develop key infrastructure, such as the provision of acute care resources. Yet, even basic descriptive information on urban acute care supply and demand is extremely limited. We therefore conducted a pilot assessment across seven diverse urban settings across the world.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.441
Teacher spread0.414 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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