The Effect of Test Position on Lumbar Spine Position Sense
Bibliographic record
Abstract
STUDY DESIGN: Repeated-measures experimental design. OBJECTIVES: The purpose of this study was to compare lumbar spine position sense in 3 test positions (standing, sitting, and 4-point kneeling [FPK]) to determine if position sense is affected by test position. BACKGROUND: Several recent studies have tested position sense in the spine. There has, however, been no consistency in the testing methods or test positions used in these studies. METHODS AND MEASURES: Seventy asymptomatic males (range, 20-51 years) volunteered for testing. Active lumbar spine repositioning accuracy and precision was tested 3-dimensionally in 3 test positions (standing, sitting, and FPK) and under 2 conditions (eyes open and blindfolded), using the neutral spine posture as the initial reference position. RESULTS: Both the accuracy and precision of lumbar spine repositioning was found to be significantly affected by test position. Repositioning errors (reflective of accuracy) were significantly larger in FPK than in both sitting and standing, and significantly larger in sitting than in standing, under both eyes-open and blindfolded conditions. Precision of repositioning was significantly less in the FPK position as compared to the standing position. CONCLUSION: The results of this study suggest that test position has a significant effect on the acuity of lumbar spine position sense and should be considered when examining the current literature on spine proprioception.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".