Descriptive patient data as an explanation for the variation in average daily costs in intensive care
Bibliographic record
Abstract
Intensive care patients require therapy that can vary considerably in type, duration and cost, so making it extremely difficult to predict patient resource use. Few studies measure actual costs; usually average daily costs are calculated and these do not reflect the variation in resource use between individual patients. The aim of this study was to analyse a data set of 193 critically ill adult patients to look for associations between routinely collected descriptive data and patient-specific costs. Regression analysis was used to explore any relationships between average daily patient-specific costs and the following variables: duration of intensive care unit stay, Acute Physiology and Chronic Health Evaluation II scores in the first 24 h, gender, age, mechanical ventilation at any point during the stay, postoperative status, emergency admission and mortality. Overall, this analysis explained 33.6% of the variation in average daily costs. The additional costs of an extra day of care, mechanical ventilation, an extra point on the Acute Physiology and Chronic Health Evaluation II score, and survival were obtained.
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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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".