Clinical methods for quantifying body segment posture: a literature review
Bibliographic record
Abstract
PURPOSE: Clinicians commonly assess posture in persons with musculoskeletal disorders and tend to do so subjectively. Evidence-based practice requires the use of valid, reliable and sensitive tools to monitor treatment effectiveness. The purpose of this article was to determine which methods were used to assess posture quantitatively in a clinical setting and to identify psychometric properties of posture indices measured from these methods or tools. METHODS: We conducted a comprehensive literature review. Pertinent databases were used to search for articles on quantitative clinical assessment of posture. Searching keywords were related to posture and assessment, scoliosis, back pain, reliability, validity and different body segments. RESULTS: We identified 65 articles with angle and distance posture indices that corresponded to our search criteria. Several studies showed good intra- and inter-rater reliability for measurements taken directly on the persons (e.g., goniometer, inclinometer, flexible curve and tape measurement) or from photographs, but the validity of these measurements was not always demonstrated. CONCLUSION: Taking measurements of all body angles directly on the person is a lengthy process and may affect the reliability of the measurements. Measurement of body angles from photographs may be the most accurate and rapid way to assess global posture quantitatively in a clinical setting.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".