Quantitative physical performance tests can effectively detect Degenerative Cervical Myelopathy: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose This review aimed to identify effective physical performance tests (PPT) as clinical outcome indicators for detecting and monitoring degenerative cervical myelopathy (DCM). Methods A comprehensive literature search was performed on seven electronic databases on the effectiveness in detection and monitoring of DCM by PPT. All included studies were reviewed and undergone quality assessments on the risk-of-bias by Newcastle-Ottawa Scale and were pooled by random-effect analysis with level of significance at 0.05. Homogeneity among studies was assessed by I2-statistics and effect of PPT was confirmed by Cohen’s d effect size and confidence intervals. Results Totally, 3111 articles were retrieved, and 19 studies were included for review and meta-analysis. There were 13 studies investigating PPT regarding the upper limbs and 12 studies regarding the lower limbs. Performance in 10-second-Grip-and-Release Test (G&R) and 9-Hole-Peg Test (9HPT) was studied in 10 and 3 articles, respectively, while 10-second-Stepping Test (SST), 30-meter-Walking Test (30MWT) and Foot-Tapping Test (FTT) for lower limbs were studied in 5, 4, and 3 articles correspondingly. Only 1 study utilized the Triangle-Stepping Test. High-quality study with fair risk-of-bias was revealed from Newcastle-Ottawa scale. Large effect size facilitated detection and monitoring in DCM was unveiling for G&R, 9HPT, SST, and 30MWT. FTT, while also effective, was hindered by a high-degree heterogeneity in the meta-analysis. Conclusion Effective PPT including G&R, 9HPT, SST, 30MWT, and FTT was identified for disease detection and monitoring in DCM.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.009 | 0.008 |
| 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) | 0.004 | 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".