Pain Assessment and Management in Critically Ill Intubated Patients: a Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Little research has been done on pain assessment in critical care, especially in patients who cannot communicate verbally. OBJECTIVES: To describe (1) pain indicators used by nurses and physicians for pain assessment, (2) pain management (pharmacological and nonpharmacological interventions) undertaken by nurses to relieve pain, and (3) pain indicators used for pain reassessment by nurses to verify the effectiveness of pain management in patients who are intubated. METHODS: Medical files from 2 specialized healthcare centers in Quebec City, Quebec, were reviewed. A data collection instrument based on Melzack's theory was developed from existing tools. Pain-related indicators were clustered into nonobservable/subjective (patients' self-reports of pain) and observable/objective (physiological and behavioral) categories. RESULTS: A total of 183 pain episodes in 52 patients who received mechanical ventilation were analyzed. Observable indicators were recorded 97% of the time. Patients' self-reports of pain were recorded only 29% of the time, a practice contradictory to recommendations for pain assessment. Pharmacological interventions were used more often (89% of the time) than nonpharmacological interventions (<25%) for managing pain. Almost 40% of the time, pain was not reassessed after an intervention. For reassessments, observable indicators were recorded 66% of the time; patients self-reports were recorded only 8% of the time. CONCLUSIONS: Pain documentation in medical files is incomplete or inadequate. The lack of a pain assessment tool may contribute to this situation. Research is still needed in the development of tools to enhance pain assessment in critically ill intubated patients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".