Sensitivity of clinical assessments of sagittal head posture
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Historically, clinicians visually evaluate posture using anatomical landmarks. Advances in technology made digital photographs now feasible to use in clinical practice. Photogrammetry may increase the reliability of the assessment of postural changes. However, differences between visually estimated and photogrammetric recorded changes in posture need to be tested. The objective of this study was to evaluate the sensitivity of visual assessments of changes in head posture in the sagittal plane in relation to photogrammetric recorded data. METHODS: The head posture of 29 female subjects in a sagittal plane was assessed visually and photogrammetrically. The visual assessment of head posture was conducted using a postural grid with a plumb for checking the alignment. The patients were classified as having forward head posture (FHP), slight FHP or no FHP. Photogrammetry of head posture was performed using the Alcimage software (Alcimar B. Soares, Uberlândia, MG, Brasil). Three reference points were used to measure the head posture angle: mentus, external auditory meatus and manubrium. The visually classified groups were compared in relation to the photogrammetric angles using one-way ANOVA. RESULTS: A significant difference was found between the FHP and no FHP groups (P = 0.001), and between the FHP and slight FHP groups (P = 0.002). However, no significant difference was found between the slight FHP and no FHP groups. CONCLUSIONS: Visual assessments of sagittal head posture were sensitive to detect differences between no FHP and FHP groups, but were not sensitive to detect differences between no FHP and slight FHP groups. Head posture photogrammetry is recommended to quantitatively detect less evident differences in head posture.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".