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International Programme for Resource Use in Critical Care (IPOC) – a methodology and initial results of cost and provision in four European countries

2005· article· en· W1532390681 on OpenAlexaff
Daniela Negrini, L. Sheppard, Gary Mills, Philip Jacobs, John Rapoport, Richard S Bourne, Bertrand Guidet, Ákos Csomós, T. Prien, Gerard F. Anderson, D. L. Edbrooke

Bibliographic record

VenueActa Anaesthesiologica Scandinavica · 2005
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineActivity-based costingIntensive careObservational studyGermanResource usePurchasingHealth carePurchasing powerPurchasing power parityEnvironmental healthEmergency medicineMedical emergencyOperations managementAccountingEconomic growthFinanceIntensive care medicineEnvironmental resource managementBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: A standardized top-down costing method is not currently available internationally. An internally validated method developed in the UK was modified for use in critical care in different countries. Costs could then be compared using the World Health Organization's Purchasing Power Parities (WHO PPPs). METHODS: This was an observational, retrospective, cross-sectional, multicentre study set in four European countries: France, UK, Germany and Hungary. A total of 329 adult intensive care units (ICUs) participated in the study. RESULTS: The costs are reported in international dollars ($) derived from the WHO PPP programme. The results show significant differences in resource use and costs of ICUs over the four countries. On the basis of the sum of the means for the major components, the average cost per patient day in UK hospitals was $1512, in French hospitals $934, in German hospitals $726 and in Hungarian hospitals $280. CONCLUSIONS: The reasons for such differences are poorly understood but warrant further investigation. This information will allow us to better adjust our measures of international ICU costs.

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 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)0.001
metaresearch head score (Gemma)0.002
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 score0.272
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.272
GPT teacher head0.427
Teacher spread0.155 · 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 teacher head, 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

Citations48
Published2005
Admission routes1
Has abstractyes

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