Urgent need for standardised guidelines for reporting healthcare costs in ICUs – Results of an integrative review of costing methodologies
Bibliographic record
Abstract
OBJECTIVES: Diverse costing methodologies in critical care have produced discrepant results. We aimed to critically review studies addressing critical care patients' costs, to estimate total costs and cost categories and to delineate methodologies used and relevant limitations. METHODS: Integrative review based on key-word searches of electronic databases targeting primary studies that report estimates of patient cost, in the last 21 years. We assessed the level transparency of reporting and the quality of the studies, by the SIGN tool. RESULTS: Overall, 12 research articles were included, of which eight studies mentioned the specific approach used to identify the elements of cost. Most studies employed a micro-costing and one study a macro-costing approach. With regard to approaches to valuation of cost components, only one study identified the bottom-up approach. The total patient cost ranged from US$ 487 to US$ 39,300 and human resources was identified as the cost category mostly driving total costs. CONCLUSIONS: Although valid methodologies to evaluate critical care patients' costs, such as micro-costing, are employed more frequently, a variety of non-standardized methods are still used. There is a pressing need to develop standardised guidelines for reporting of observational studies of cost in healthcare, with particular considerations for critical care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".