Surveys of post-operative pain management in a teaching hospital in Rwanda — 2013 and 2017
Bibliographic record
Abstract
Background Postoperative pain management (POPM) appeared to be weak in Rwanda.Aims The aim of this study was to compare POPM measures in a teaching hospital between 2013 and 2017.Methods A two-phase observational study in 2013 and 2017. was conducted. Participants were recruited prior to major surgery and followed for two postoperative days. A numerical rating scale (0–10) was administered to all participants in both years, and the International Pain Outcomes questionnaire was administered in 2017. Recruitment, consent, and data collection were performed in participants’ preferred language.Results One hundred adult participants undergoing major general, gynecologic, orthopedic, or urologic surgery were recruited in 2013 and 83 were recruited in 2017. Fourteen percent of participants in 2013 and 46% in 2017 scored their worst pain as severe (>6; P < 0.001). This was despite improved preoperative recognition of patients at high risk for severe postoperative pain (those with chronic pain or preoperative pain); 27% and 0% of these patients were not documented in 2013 and 2017, respectively (P = 0.006). Other measures of improved planning included “any preoperative discussion of POPM” (P < 0.001) and “discussion of POPM options” (P = 0.002). Preemptive analgesia use increased (3% of participants in 2013 and 54% in 2017; P < 0.001). Incidence of participants having no postoperative analgesic at all decreased from 25% in 2013 to 5% in 2017 (P < 0.001).Conclusions Though severe postoperative pain incidence did not improve from 2013 to 2017, POPM improved by a number of measures. These changes may be attributed to pain research conducted there having raised awareness.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".