Implications for the Use of Postural Analysis as a Clinical Diagnostic Tool: Reliability of Quantifying Upright Standing Spinal Postures From Photographic Images
Bibliographic record
Abstract
OBJECTIVES: A repeated measures design was used to test the reliability of standing spine postures within subjects using a biologically relevant measure determined by digitization of images and to compare the results to a previously tested vertical reference method. METHODS: Twenty subjects attended 3 sessions consisting of 5 trials each. Photographs of the sagittal and posterior views of normal upright standing were taken. Landmarks were digitized and cervical, thoracic, and lumbar spinal angles were calculated using the algebraic dot product. Intraclass correlation coefficients were used to evaluate intrasubject reliability across sessions. RESULTS: According to the intraclass correlation coefficients, posture had good to excellent reliability in the sagittal view and provided a more stable measure of spinal angles than the posterior view. Mean repositioning errors were less than 6 degrees and 2 degrees in the sagittal and posterior views, respectively. CONCLUSIONS: Although the repeatability of posture was improved in the sagittal view, when a biological measure was used instead of an external vertical reference to calculate spinal angles, individual subject posture was still variable. This brings into question the effectiveness and validity of using surface skin markers to track postural changes due to clinical interventions. If the postural analysis approach is to be used to detect changes due to clinical treatment, such changes must be larger than the baseline repositioning errors seen in healthy subjects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".