Pain Assessment and Management in Critically Ill Intubated Patients: a Retrospective Study
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it