Psychometric Properties of the Patient-Specific Functional Scale in Patients with Low Back Pathology: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: This systematic review and meta-analysis identifies, critically appraises, synthesizes, and meta-analyses the reported psychometric properties of the Patient-Specific Functional Scale (PSFS) in patients with low back pain or pathology. Method: The MEDLINE, Embase, PubMed, and Google Scholar databases were searched from their inception to September 2019. We included prospective measurement studies that reported on the psychometric properties (reliability, validity, responsiveness) of the PSFS in people with low back pain or pathology. We followed the COnsensus-based Standards for the selection of health Measurement INstruments 2018 guideline for systematic reviews. We performed both quantitative and qualitative syntheses in which the results were summarized on the basis of the reported measurement properties and study quality. Results: Ten eligible studies were included. The pooled PSFS reliability measure was excellent (intra-class correlation coefficient = 0.89; 95% CI: 0.75, 0.95). Validity measures displayed correlations that ranged from −0.47 to 0.69 when compared with other patient-reported outcome measures (PROMs) or other tests. Eight studies had assessed the responsiveness of the PSFS. Effect sizes reported were large (≥ 0.91). Conclusions: The PSFS is a reliable, valid, and responsive PROM for patients with low back pain or pathology.
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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 | 0.032 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".