The reliability of quantifying upright standing postures as a baseline diagnostic clinical tool
Bibliographic record
Abstract
OBJECTIVE: To assess the reliability of posture across and within subjects, specifically the repeatability of spinal angles determined by digitization of images in the anterior, posterior, and sagittal views. DESIGN: A repeated measure design was used in which subjects were required to attend 3 sessions, each consisting of 3 trials. Photographs of the anterior, posterior, and lateral views of normal, relaxed upright standing were taken during each trial. Landmarks were digitized and cervical, thoracic, and lumbar angles were calculated with respect to a vertical reference line. SUBJECTS: Fourteen healthy and active subjects (7 male subjects and 7 female subjects) were recruited from a university student population. All had been free of low back pain during the previous 6 months. RESULTS: When comparing mean angles, no significant differences were detected for any angle in any view. However, large variability within subjects was observed, likely leading to the lack of significance found with respect to the main factors in the analysis of variance (ANOVA). Large coefficients of variance (CVs) reflect the substantial intrasubject variability, as well as poor to moderate agreement indicated by intraclass correlation coefficients (ICCs). There were no apparent trends indicating that gender affected repeatability of posture. CONCLUSIONS: The poor repeatability of postures documented using the studied method brings into question the validity of this postural analysis approach for either diagnostic use or tracking changes in response to treatment. Users of such postural analysis tools should interpret postural deviations from a vertical reference with caution, as there are many inherent factors that can contribute to the variability of these measured postures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".