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Record W170446710 · doi:10.1177/0310057x1003800124

A Unique Snapshot of Intensive Care Resources in Australia and New Zealand

2010· article· en· W170446710 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnaesthesia and Intensive Care · 2010
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsCentre for Advancing Health Outcomes
FundersIntensive Care Society
KeywordsMedicineIntensive carePublic sectorPrivate sectorPopulationDescriptive statisticsFamily medicineEnvironmental healthIntensive care medicineEconomic growthStatistics

Abstract

fetched live from OpenAlex

The objective of this study was to analyse and report on the distribution and attributes of intensive care services in Australia and New Zealand for the 2005/2006 financial year A survey was mailed to 155 Australian and 26 New Zealand intensive care units (ICU) listed on the database of the Australian and New Zealand Intensive Care Society. A descriptive analytical approach was used. Of the 181 ICUs, 177 provided data. In Australia there were 100 public sector and 51 private sector ICUs and in New Zealand, 24 public sector and two private sector ICUs. These units contain 1485 available beds in the public sector and 538 available beds in the private sector Calculations to determine beds per 100,000 population, medical specialists per 1000 patient days and registered nurses per 1000 patient days showed wide variation. International comparisons are limited by lack of data; however it does appear that intensive care patients in Australia and New Zealand have very good outcomes.

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.

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)0.000
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 score0.099
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.058
GPT teacher head0.329
Teacher spread0.272 · 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