Monitoring the evolutionary process of quality: Risk-adjusted charting to track outcomes in intensive care
Bibliographic record
Abstract
OBJECTIVE: To present graphical procedures for prospectively monitoring outcomes in the intensive care unit. DESIGN: Observational study: risk-adjusted control chart analysis of a case series. SETTING: Tertiary referral adult intensive care unit: Princess Alexandra Hospital, Brisbane, Australia. PATIENTS: A total of 3398 intensive care unit admissions from January 1, 1995, to January 1, 1998. CONCLUSIONS: Risk-adjusted process control charting procedures for continuous monitoring of intensive care unit outcomes are proposed as quality management tools. A modified Shewhart p chart and cumulative sum process control chart, using the Acute Physiology and Chronic Health Evaluation III model mortality prediction for risk adjustment, are presented. The risk-adjusted p chart summarizes performance at arbitrary intervals and plots observed against predicted mortality rate to detect large changes in risk-adjusted mortality. The risk-adjusted cumulative sum procedure is a likelihood-based scoring method that adjusts for estimated risk of death, accumulating evidence from outcomes of all previous patients. It formally tests the hypothesis of a change in the odds of death. In this application, we detected a decrease from above to predicted risk-adjusted mortality. This was temporally related to increased senior staffing levels and enhanced ongoing multidisciplinary review of practice, quality improvement, and educational activities. Formulas and analyses are provided as appendices.
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".