A preliminary study of 24-hour post-cesarean patient controlled analgesia: postoperative pain reports and morphine requests/utilization are greater in abstaining smokers than non-smokers.
Bibliographic record
Abstract
BACKGROUND: Previous clinical studies have not examined the relationship between nicotine abstinence and opioid use for postoperative analgesia. This may be important because tobacco smokers are routinely required to abstain from smoking just before and during acute post-surgical recovery. This study investigated IV morphine self-administration [patient controlled analgesia (PCA)], subjective pain/drug effects and other measures during post-operative (elective Cesarean section) recovery. MATERIAL/METHODS: Seven females, selected to vary in nicotine use [4 non-using controls (CON), 3 users (NIC)], completed the protocol. Gender, time and type of surgery, and pre- and intra-operative medications were controlled. Subject assessments included the McGill Pain Questionnaire and the Profile of Mood States; drug effects were measured using the Addiction Research Center Inventory. RESULTS: Mean (M +/-SD) 24-hr morphine responding (button-pressing requests) was significantly higher for NIC (M=183+/-50) than CON (M=38+/-10). Weight-adjusted morphine use (mg/kg/24 hr) was significantly higher for NIC (M=1.80+/-0.23) than CON (M=0.64+/-0.14). Although the groups reported similar pain severity following morphine loading, NIC patients reported significantly greater pain severity than CON patients after 24 hr PCA. CONCLUSIONS: These preliminary data suggest that a history of nicotine use and/or short-term nicotine abstinence can modulate morphine use and analgesia during post-operative recovery. These procedures provide a model for studying patterns and determinants of analgesic self-administration in medical settings.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".