Evaluation of a method to quantify posture and scapula position using biplanar radiography
Bibliographic record
Abstract
BACKGROUND: Recent studies have stated the relevance of having new parameters to quantify the position and orientation of the scapula with patients standing upright. Although biplanar radiography can provide 3D reconstructions of the scapula and the spine, it is not yet possible to acquire these images with patients in the same position. METHODS: Two pairs of images were acquired, one for the 3D reconstruction of the spine and ribcage and one for the 3D reconstruction of the scapula. Following 3D reconstructions, scapular alignment was performed in two stages, a coarse alignment based on manual annotations of landmarks on the clavicle and pelvis, and an adjusted alignment. Clinical parameters were computed: protraction, internal rotation, tilt and upward rotation. Reproducibility was assessed on an in vivo dataset of upright biplanar radiographs. Accuracy was assessed using supine cadaveric CT-scans and digitally reconstructed radiographs. FINDINGS: The mean error was less than 2° for all clinical parameters, and the 95 % confidence interval for reproducibility ranged from 2.5° to 5.3°. INTERPRETATION: The confidence intervals were lower than the variability measured between participants for the clinical parameters assessed, which indicates that this method has the potential to detect different patterns in pathological populations.
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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 | 0.008 | 0.017 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".