Pain experiences of traumatically injured patients in a critical care setting
Bibliographic record
Abstract
BACKGROUND: Little is known about the acute pain experiences of traumatically injured critically ill patients. OBJECTIVES: To describe pain experiences of traumatically injured adults during the first 72 hours of hospitalization. METHODS: Thirty multiply injured adults at a level I trauma center participated in the study. Pain was measured by using the McGill Pain Questionnaire and a visual analog scale. Subjects completed pain measures while at rest in a supine recumbent position and after a turn onto the side. RESULTS: The typical subject was 37 years old, had 4 major blunt trauma injuries, and had received the equivalent of 55.9 mg of morphine during the 24 hours before data collection. Mean at-rest scores were 26.5 on the pain-rating index, 2 on the present pain intensity index, and 34.6 on the visual analog scale. Immediately after the turn, mean scores on the visual analog scale increased from 25 to 48.1 (P = .002). Other pain scores after the turn did not differ significantly from at-rest values. Subjects who turned had lower scores on the visual analog scale at rest (P = .02) and less anxiety (P = .02) than did those who refused to turn. Ninety-six percent reported pain in the injured areas, and 36% reported pain related to biomedical devices. No relationship was found among reported pain and demographic, treatment, or clinical variables. CONCLUSIONS: Additional research is needed on pain at rest and during commonly performed procedures and on improved methods for pain relief in traumatically injured critically ill 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".