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tOBJECTIVE: We aimed to quantify the proportion not attributable to the specific effects (PCE) of physical therapy interventions for musculoskeletal pain. tDESIGN: Intervention systematic review with meta-analysis. tLITERATURE SEARCH: We searched Ovid, MEDLINE, EMBASE, CINAHL, Scopus, PEDro, Cochrane Controlled Trials Registry, and SPORTDiscus databases from inception to April 2023. tSTUDY SELECTION CRITERIA: Randomized placebo-controlled trials evaluating the effect of physical therapy interventions on musculoskeletal pain. tDATA SYNTHESIS: Risk of bias was evaluated using the Cochrane risk-of-bias tool for randomized trials (RoB 2). The proportion of physical therapy interventions effect that was not explained by the specific effect of the intervention was calculated, using the proportion not attributable to the specific effects (PCE) metric, and a quantitative summary of the data from the studies was conducted using the random-effects inverse-variance model (Hartung-Knapp-Sidik-Jonkman method). tRESULTS: Sixty-eight studies were included in the systematic review (participants: n = 5238), and 54 placebo-controlled trials informed our meta-analysis (participants: n = 3793). Physical therapy interventions included soft tissue techniques, mobilization, manipulation, taping, exercise therapy, and dry needling. Placebo interventions included manual, nonmanual interventions, or both. The proportion not attributable to the specific effects of mobilization accounted for 88% of the immediate overall treatment effect for pain intensity (PCE = 0.88, 95% confidence interval [CI]: 0.57, 1.20). In exercise therapy, this proportion accounted for 46% of the overall treatment effect for pain intensity (PCE = 0.46, 95% CI: 0.41, 0.52). The PCE in manipulation excelled in short-term pain relief (PCE = 0.81, 95% CI: 0.62, 1.01) and in mobilization in long-term effects (PCE = 0.86, 95% CI: 0.76, 0.96). In taping, the PCE accounted for 64% of disability improvement (PCE = 0.64, 95% CI: 0.48, 0.80). tCONCLUSION: The outcomes of physical therapy interventions for musculoskeletal pain were significantly influenced by factors not attributable to the specific effects of the interventions. Boosting these factors consciously to enhance therapeutic outcomes represents an ethical opportunity that could benefit patients. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To compare the accuracy of an artificial intelligence chatbot to clinical practice guidelines (CPGs) recommendations for providing answers to complex clinical questions on lumbosacral radicular pain. DESIGN: Cross-sectional study. METHODS: We extracted recommendations from recent CPGs for diagnosing and treating lumbosacral radicular pain. Relative clinical questions were developed and queried to OpenAI's ChatGPT (GPT-3.5). We compared ChatGPT answers to CPGs recommendations by assessing the (1) internal consistency of ChatGPT answers by measuring the percentage of text wording similarity when a clinical question was posed 3 times, (2) reliability between 2 independent reviewers in grading ChatGPT answers, and (3) accuracy of ChatGPT answers compared to CPGs recommendations. Reliability was estimated using Fleiss' kappa (k) coefficients, and accuracy by interobserver agreement as the frequency of the agreements among all judgments. RESULTS: We tested 9 clinical questions. The internal consistency of text ChatGPT answers was unacceptable across all 3 trials in all clinical questions (mean percentage of 49%, standard deviation of 15). Intrareliability (reviewer 1: k = 0.90, standard error [SE] = 0.09; reviewer 2: k = 0.90, SE = 0.10) and interreliability (k = 0.85, SE = 0.15) between the 2 reviewers was "almost perfect." Accuracy between ChatGPT answers and CPGs recommendations was slight, demonstrating agreement in 33% of recommendations. CONCLUSION: ChatGPT performed poorly in internal consistency and accuracy of the indications generated compared to clinical practice guideline recommendations for lumbosacral radicular pain. [ABSTRACT FROM AUTHOR]