Behavioral Pain Assessment Tool for Critically Ill Adults Unable to Self-Report Pain
Bibliographic record
Abstract
BACKGROUND: Critically ill adults often cannot self-report pain. OBJECTIVE: To determine the effect of the Critical-Care Pain Observation Tool on frequency of documentation of pain assessment and administration of analgesics and sedatives in critically ill patients unable to self-report pain. METHODS: Data on patients in 2 intensive care units of a university-affiliated hospital were collected before and after implementation of the tool. Patients were prospectively screened for eligibility; data were extracted retrospectively. RESULTS: Data were recorded for a maximum of 72 hours before and after implementation of the tool in the cardiovascular intensive care unit (130 patients before and 132 after) and in the medical/surgical/trauma unit (59 patients before and 52 after). Proportion of pain assessment intervals with pain assessment documented increased from 15% to 64% (P < .001) in the cardiovascular unit and from 22% to 80% (P < .001) in the other unit. Median total dose of opioid analgesics decreased from 5 mg to 4 mg in the cardiovascular unit (P = .02) and increased from 27 mg to 75 mg (P = .002) in the other unit. Median total dose of benzodiazepines decreased from 12 mg to 2 mg (P < .001) in the cardiovascular unit and remained unchanged in the other unit. Increased documentation of pain assessment was associated with increased age in the cardiovascular unit and with decreased maximum scores on the Sequential Organ Failure Assessment in the other unit. CONCLUSION: Implementation of the tool increased frequency of pain assessment and appeared to influence administration of analgesics in both units.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".