Reducing the Pain: A Systematic Review of Postdischarge Analgesia Following Elective Orthopedic Surgery
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine which analgesic modalities used following discharge have the greatest efficacy in reducing postoperative pain after elective non-axial orthopedic surgery. DESIGN AND SETTING: A systematic review was conducted using the databases CENTRAL, MEDLINE, and EMBASE, as well as clinical practice guidelines databases and trial registries. Titles and abstracts were perused by two reviewers for randomized clinical trials in English fulfilling inclusion and exclusion criteria. Quality assessments, including the Oxford Quality Score, selective reporting, and sources of funding, were also performed. OUTCOME MEASURES: Pain intensity/relief, global patient evaluation, and use of rescue analgesia, as well as adverse events and withdrawals. RESULTS: 2,167 articles were retrieved and 23 articles were eligible for inclusion. They investigated analgesic modalities including alternative therapies (5); cyclooxygenase-2 inhibitors (3); nonselective, nonsteroidal anti-inflammatory drugs (NSAIDs) (12); opioids (2); and other pharmaceutical classes (1). Cycooxygenase-2 inhibitors and opioids demonstrated significant efficacy with minimal side effects. Most nonselective NSAIDs were effective analgesics but had a poorer side-effect profile. Alternative therapies demonstrated no significant efficacy. CONCLUSIONS: Opioids and cyclooxygenase-2 inhibitors are effective in providing analgesia in the extended postoperative period following orthopedic surgery with a minimal side-effect profile, while nonselective NSAIDs need to be treated with caution. Homeopathy is not an effective analgesic, while acupuncture has varied evidence and effectiveness. Treatment of postoperative fatigue may also improve analgesia control. This study provides orthopedic surgeons with a basis for evidence-based prescribing of postdischarge analgesia. However, further studies to validate these results against modern reporting standards are needed.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".