Baseline individual factors associated with clinical outcomes in adults with non-specific low back pain following manual therapy: a systematic review
Bibliographic record
Abstract
BACKGROUND: Primary care providers consider the identification of patient subgroups as a high research priority. Unfortunately, evidence to support the benefit of treatments targeting subgroups of patients with NSLBP remains inconsistent. Specifically, little is known about baseline individual patient characteristics associated with optimal clinical improvement from manual therapy. This systematic review aims to identify baseline individual factors (BIFs), including patient characteristics, self-reported questionnaires, clinical examination, and ancillary test factors associated with clinical improvement (or lack of) among adult patients with Non-Specific Low Back Pain (NSLBP) following manual therapy. METHODS: A systematic review of published evidence in Medline, Embase, Cochrane, Index To Chiropractic Literature, and CINAHL was conducted until April 2024. Studies included participants aged 18 years and over with NSLBP and without radiculopathy. Participants received manual therapies, including musculoskeletal manipulation/mobilization (spinal and extremities) and soft tissue therapy. We excluded mechanically assisted manipulations and interventions mainly involving exercise, education, and/or advice. Two independent assessors screened studies for inclusion, extracted data, and assessed risks of bias using the Quality In Prognosis Studies (QUIPS) Tools. A qualitative synthesis of findings was undertaken. BIFs were synthesized according to patient-reported outcomes measure domains: 1) pain intensity measures, 2) disability measures, 3) global perceived effect, and 4) other factors (e.g., satisfaction with care, total number of visits). RESULTS: Data from 19 studies (reported in 21 articles) involving 4,689 participants were analyzed. Twelve studies reported pain intensity, 18 reported disability outcomes, and 4 reported patient's global perceived effect. Over 70% of the included studies had a high risk of confounding bias. Included studies explored the potential association between clinical outcomes and 172 BIFs. BIFs were categorized into patient characteristics (n = 40), self-reported questionnaire (n = 31), clinical examination (n = 82), and ancillary tests (n = 20). Fourteen multivariate models explored the association with clinical improvement, and four others investigated the association with non-improvement. Findings were inconsistent across studies. CONCLUSION: Using BIFs in clinical practice to predict clinical outcomes following manual therapy treatment appears to be premature. Future studies should aim to replicate the results and differentiate prognostic factors from treatment effect modifiers. TRIAL REGISTRATION: CRD42019131416.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".