Increasing Reliability of APACHE II Scores in a Medical-Surgical Intensive Care Unit: A Quality Improvement Study
Bibliographic record
Abstract
BACKGROUND: Given their clinical, research, and administrative purposes, scores on the Acute Physiology and Chronic Health Evaluation (APACHE) II should be reliable, whether calculated by health care personnel or a clinical information system. OBJECTIVE: To determine reliability of APACHE II scores calculated by a clinical information system and by health care personnel before and after a multifaceted quality improvement intervention. METHODS: APACHE II scores of 37 consecutive patients admitted to a closed, 15-bed, university-affiliated intensive care unit were collected by a research coordinator, a database clerk, and a clinical information system. After a quality improvement intervention focused on health care personnel and the clinical information system, the same methods were used to collect data on 32 consecutive patients. The research coordinator and the clerk did not know each other's scores or the information system's score. The data analyst did not know the source of the scores until analysis was complete. RESULTS: APACHE II scores obtained by the clerk and the research coordinator were highly reliable (intraclass correlation coefficient, 0.88 before vs 0.80 after intervention; P = .25). No significant changes were detected after the intervention; however, compared with scores of the research coordinator, the overall reliability of APACHE II scores calculated by the clinical information system improved (intraclass correlation coefficient, 0.24 before intervention vs 0.91 after intervention, P < .001). CONCLUSIONS: After completion of a quality improvement intervention, health care personnel and a computerized clinical information system calculated sufficiently reliable APACHE II scores for clinical, research, and administrative purposes.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".