Development of Active Hip Abduction as a Screening Test for Identifying Occupational Low Back Pain
Bibliographic record
Abstract
STUDY DESIGN: Analytic observational prospective study performed in a controlled laboratory setting. OBJECTIVES: To assess the ability of a new screening tool, the active hip abduction test, to predict low back pain development during prolonged standing in previously asymptomatic individuals. BACKGROUND: Most screening tools used for a patient with low back pain do not assess the patient's ability to maintain postural control in the frontal plane, when placed in an unstable position. Postural-control differences in pain developers, as compared to non-pain developers, during standing have been found previously. An attempt was made to predict pain development with a simple screening test. METHODS: Forty-three previously asymptomatic volunteers underwent a clinical assessment prior to a 2-hour standing protocol designed to induce low back pain. Participants rated low back pain with a visual analog scale and were classified into pain developers or non-pain developers. RESULTS: Forty percent of participants developed low back pain. The active hip abduction test was the only test that discriminated between pain-developer groups. When the examiner scored the test, the odds ratio was 3.85 (95% confidence interval [CI]: 1.05-19.07), and when the test was self-rated, the odds ratio was 6.55 (95% CI: 1.14-37.75) for pain development during standing. CONCLUSION: The active hip abduction test appears to show promise for predicting individuals who are at risk for low back pain development during prolonged standing. More work is required to validate the test in clinical populations, and to assess interrater and intrarater reliability.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".