Physical functioning outcome measures in the lumbar spinal surgery population and measurement properties of the physical outcome measures: protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Low back pain can lead to substantial decline in physical functioning. For disabling pain not responsive to conservative management, surgical intervention can enhance physical functioning. Measurements of physical functioning include patient-reported outcome measures and physical outcome measures using evaluations of impairments, performance on a standardised task or activity in a natural environment. Selecting outcome measures with adequate measurement properties is fundamental to evaluating effectiveness of interventions. The purpose of this systematic review is to identify outcome measures (patient reported and physical) used to evaluate physical functioning (stage 1) and assess the measurement properties of physical outcome measures of physical functioning (stage 2) in the lumbar spinal surgery population. METHODS AND ANALYSIS: This protocol aligns with the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines and Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. Using a two-staged approach, searches will be performed in MEDLINE, EMBASE, Health and Psychosocial Instruments, CINAHL, Web of Science, Scopus, PEDro and the grey literature from inception until 15 December 2021. Stage 1 will identify studies evaluating physical functioning with patient-reported or physical outcome measures in the lumbar spinal surgery population. Stage 2 will search for studies evaluating measurement properties (validity, reliability, responsiveness) of the physical outcome measures identified in stage 1 in the lumbar spinal surgery population. Two independent reviewers will evaluate studies for inclusion, extract data, assess risk of bias (COSMIN risk of bias tool and checklist) and quality of evidence (modified Grading of Recommendations Assessment, Development and Evaluation approach). Results for each measurement property per physical outcome measure will be quantitatively pooled if there is adequate clinical and methodological homogeneity or qualitatively synthesised if there is high heterogeneity in studies. ETHICS AND DISSEMINATION: Ethics approval is not required. Results will be disseminated through peer-reviewed journal publication and conference presentation. PROSPERO REGISTRATION NUMBER: CRD42021293880.
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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 | 0.092 | 0.160 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.022 | 0.021 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.064 | 0.010 |
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".