Prevalence of postoperative pain after hospital discharge: systematic review and meta-analysis
Bibliographic record
Abstract
Assessment and management of postoperative pain after hospital discharge is very challenging. We conducted a systematic review to synthesize available evidence on the prevalence of moderate-to-severe postoperative pain within the first 1 to 14 days after hospital discharge. The previously published protocol for this review was registered in PROSPERO. MEDLINE and EMBASE databases were searched until November 2020. We included observational postsurgical pain studies in the posthospital discharge setting. The primary outcome for the review was the proportion of study participants with moderate-to-severe postoperative pain (eg, pain score of 4 or more on a 10-point Numerical Rating Scale) within the first 1 to 14 days after hospital discharge. This review included 27 eligible studies involving a total of 22,108 participants having undergone a wide variety of surgical procedures. The 27 studies included ambulatory surgeries (n = 19), inpatient surgeries (n = 1), both ambulatory and inpatient surgeries (n = 4), or was not specified (n = 3). Meta-analyses of combinable studies provided estimates of pooled prevalence rates of moderate-to-severe postoperative pain ranging from 31% 1 day after discharge to 58% 1 to 2 weeks after discharge. These findings suggest that moderate-to-severe postoperative pain is a common occurrence after hospital discharge and highlight the importance of future efforts to more effectively evaluate, prevent, and treat postsurgical pain in patients discharged from the hospital.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".