How Do We Improve Sleep Quality After Total Joint Arthroplasty? A Systematic Review of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Despite the importance of sleep for physiological function, rehabilitation, and recovery, sleep quality after total joint arthroplasty (TJA) remains poor. The objective of this systematic review was to identify, summarize, and evaluate postoperative interventions aimed at improving sleep quality after TJA. METHODS: A systematic review of PubMed (MEDLINE) and Scopus (Embase, MEDLINE, COMPENDEX) from inception to April 2024 was conducted (PROSPERO ID: CRD42023447317). Randomized controlled trials on interventions to improve sleep quality were included. Sleep outcomes, including the Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, Patient-Reported Outcome Measurement Information System-Sleep Disturbance, Numeric Rating Scale sleep scores,l9 were extracted. Descriptive statistics were used to analyze the available data. RESULTS: Of the 1,549 articles identified, seven randomized trials with a total of 840 patients were included (394 total hip arthroplasties [THA], 446 total knee arthroplasties [TKA]). Pittsburgh Sleep Quality Index was the most commonly used outcome for assessing sleep quality. Among THA studies, zolpidem, combined fascia iliaca compartment block (FICB) and dexmedetomidine (DEX), and perioperative methylprednisolone were shown to markedly improve postoperative sleep quality. Neither topical cannabidiol nor topical essential oil was found to improve postoperative sleep quality after TKA. Melatonin had no effect on sleep outcomes after TJA. CONCLUSION: Zolpidem, FICB + DEX, and perioperative methylprednisolone are effective interventions to improve sleep quality after THA. Topical cannabis, topical essential oil, and melatonin did not improve sleep quality. No effective sleep interventions for TKA patients were identified. Improving sleep quality remains a potential therapeutic goal to improve patient satisfaction after TJA. Continued investigation on this topic is therefore necessary.
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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 | 0.024 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.014 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".