A comparison of patients' and nurses' assessments of pain intensity in patients with coronary artery disease
Bibliographic record
Abstract
Self-report of pain is the single most reliable indicator of pain intensity. The purpose of this study was to compare patients' and nurses' ratings of patients' pain. The sample comprised 76 patients and 65 nurses in coronary care units that rated the patient's pain intensity on a 0-10 numeric rating scale. Results showed that the mean scores of nurses were lower than their patients significantly (P < 0.01). Also, nurses assessed patients' pain intensity accurately 60% of the time. Overestimations and underestimations were 12.4% and 27.6% respectively. In addition, there were positive, moderate and significant correlations between patients' and nurses' ratings (r = 0.41, P < 0.001). Underestimation of patient's pain can have negative effects if appropriate treatment is withheld. This emphasizes the importance of a systematic assessment and acceptance of the patient's self-reported of pain.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".