The impact of mental state altering medications on preventable falls after total hip or total knee arthroplasty: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Joint replacement surgery of the lower extremities are common procedures in elderly persons who are at increased risk of postoperative falls. The use of mental state altering medications, such as opioids, antidepressants or benzodiazepines, can further contribute to impaired balance and risk of falls. The objective of the current systematic review was to evaluate the risk of the use of mental state altering medications on postoperative falls in patients undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA). METHODS: A comprehensive search of Medline, Embase and Cochrane Controlled Trials Register was conducted from 1 October 1975 to 1 September 2021. The search was repeated in may 2023 and conducted from 1 October 1975 to 1 June 2023. Clinical trials that evaluated the risk of medication on postoperative THA and TKA falls were eligible for inclusion. Articles were evaluated independently by two researchers for risk of bias using the Newcastle-Ottawa Scale. A meta-analysis was performed to determine the potential effect of postoperative use of mental state altering medications on the risk of falls. Lastly, a qualitative synthesis was conducted for preoperative mental state altering medications use. RESULTS: Seven cohort studies were included, of which five studies focussed on the postoperative use of mental state altering medications and two investigated the preoperative use. Meta-analysis was performed for the postoperative mental state altering medications use. The postoperative use of mental state altering medications was associated with fall incidents (OR: 1.81; 95% CI: 1.04; 3.17) (p < 0.01) after THA and TKA. The preoperative use of opioids > 6 months was associated with a higher risk of fall incidents, whereas a preoperative opioid prescription up to 3 months before a major arthroplasty had a similar risk as opioid-naïve patients. CONCLUSIONS: The postoperative use of mental state altering medications increases the risk of postoperative falls after THA and TKA. Prior to surgery, orthopaedic surgeons and anaesthesiologists should be aware of the associated risks in order to prevent postoperative falls and associated injuries.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| 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".