A new measurement method for spine reposition sense
Bibliographic record
Abstract
BACKGROUND: A cost effective tool for the measurement of trunk reposition sense is needed clinically. This study evaluates the reliability and validity of a new clinical spine reposition sense device. METHODS: The first part of this three part investigation included 45 asymptomatic subjects examined in the first 20 repeated trials portion assessing spine reposition sense. The second portion, test-retest, examined 57 asymptomatic subjects. Initial testing consisted of subjects sitting on the device and performing 20 trials of a self-determined 2/3 trunk flexion position. The second portion of the study involved 7 trials of trunk flexion performed twice. The angular position for each trial was calculated and the mean reposition error from the initial 2/3 position was determined. For the third portion, the new device was compared to the Skill Technologies 6D (ST6D) Imperial Motion Capture and Analysis System. RESULTS: ICC (3,1) for trials 4-7 was 0.79 and 0.76 for time one and time two, respectively and the test-retest ICC (3,k) was 0.38. Due to the poor test-retest ICC, the Bland Altman method was used to compare test and retest absolute errors. Most measurement differences were small and fell within the 95% confidence interval. Comparable measures between the two methods were found using the Bland Altman method to compare the reposition sense device to the ST6D system. CONCLUSION: The device may be a cost effective clinical technique for sagittal trunk reposition sense measurement.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".