The Relationship Between Movement-Evoked Versus Spontaneous Pain and Peak Expiratory Flow After Abdominal Hysterectomy
Bibliographic record
Abstract
UNLABELLED: The pathogenesis of postoperative lung dysfunction implies a role for movement-evoked pain (e.g., splinting/hypoventilation because of pain avoidance). However, interactions between evoked pain and respiratory physiology are poorly understood. Thus, we examined the relationship between evoked versus spontaneous pain and one index of pulmonary function. In 25 patients having undergone a hysterectomy, visual analog scale ratings (100 mm) for spontaneous pain (REST) and pain during sitting (SIT), forced expiration (BLOW), and coughing (COUGH) were measured together with peak expiratory flow (PEF) at eight time points during postoperative Days 1 and 2. Secondary outcome measures included oxygen saturation and oxygen requirements. Pain was significantly correlated with PEF for COUGH, SIT, BLOW, and REST at eight, seven, four, and two of the eight studied time points, respectively. Mean visual analog scale scores [SE] for COUGH (26.1 mm [1.7]) and SIT (21.5 mm [1.5]) were greater (P < 0.05) than REST (10.5 mm [0.8]), and COUGH was greater (P < 0.05) than BLOW (16.8 mm [1.3]). All pain measures diminished (P < 0.05), and PEF reductions improved (P < 0.05) across the study period. We hypothesize that the consistent negative correlation of COUGH-evoked pain with PEF is, in part, caused by avoidance of coughing, which ultimately limits deep inspiration, lung reexpansion, and clearance of secretions. IMPLICATIONS: Movement-evoked pain may be an important contributor to postoperative complications, but its mechanisms are poorly understood. This study provides the first evidence that postoperative evoked pain correlates with lung function and highlights the need for future research on mechanisms and implications of this phenomenon.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".