Interrater Reliability of APACHE II Scores for Medical-Surgical Intensive Care Patients: A Prospective Blinded Study
Bibliographic record
Abstract
BACKGROUND: Despite widespread use of the Acute Physiology and Chronic Health Evaluation II (APACHE II), its interrater reliability has not been well studied. OBJECTIVE: To determine interrater reliability of APACHE II scores among 1 intensive care nurse and 2 research clerks. METHODS: In a prospective, blinded, observational study, 3 raters collected APACHE II scores on 37 consecutive patients in a medical-surgical intensive care unit. One research clerk was blinded to the study's start date to minimize observer bias. The nurse and the other research clerk were blinded to each other's scores and did not communicate with the first research clerk about the study. The data analyst was blinded to the identity and source of all 3 raters' scores. Intraclass correlation coefficients and 95% confidence intervals were assessed. RESULTS: Mean (standard deviation) APACHE II scores were 21.8 (9.2) for the nurse, 20.4 (7.7) for research clerk 1, and 20.5 (8.1) for research clerk 2. Among the 3 raters, the intraclass correlation coefficient (95% confidence interval) was 0.90 (0.84, 0.94) for the APACHE II total score. Within APACHE II score components, the highest reliability was for age (0.98 [0.97, 0.99]), with lower reliabilities for the Chronic Health Index (0.64 [0.50, 0.80]) and the verbal component of the Glasgow Coma Scale (0.40 [0.20, 0.60]). Results were similar between pairs of raters. CONCLUSIONS: Use of trained nonmedical personnel to collect illness severity scores for clinical, research, and administrative purposes is reasonable. This method could be used to assess reliability of other illness severity scores.
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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 | 0.073 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".