Upright Static Pelvic Posture as Rotations and Translations in 3-Dimensional From Three 2-Dimensional Digital Images: Validation of a Computerized Analysis
Bibliographic record
Abstract
PURPOSE: The aim of this study was to determine the accuracy in measuring the pelvic orientations of a phantom model using the PosturePrint method. METHODS: In the Université du Québec à Trois-Rivières biomechanics laboratory, Trois-Rivières, Quebec, Canada, a mannequin was fixed on a rotating platform. For a set of 3 photographs (left lateral, anterior to posterior, right lateral) of each position, the mannequin pelvis was placed in 68 different postures on a stand, 61 cm from a wall, in front of a digital camera. The camera was at 83.8 cm in height and at 3.35 m from a calibrated wall grid. Mannequin postures were in 5 degrees of freedom: lateral translation (Tx), lateral flexion (Rz), axial rotation (Ry), flexion-extension (Rx), and anterior-posterior translation (Tz). Average errors were the differences of the positioned postures to the PosturePrint computed values. RESULTS: Mean and SD of computational errors for rotation displacements were Rx = 0.5 degrees +/- 0.8 degrees , Ry = 1.3 degrees +/- 0.8 degrees , and Rz = 0.5 degrees +/- 0.3 degrees , and for translation, Tz = 1.2 +/- 0.6 mm and Tx = 0.9 +/- 0.5 mm. CONCLUSIONS: The PosturePrint system allowed for accurate postural measurement of rotations and translations of a mannequin pelvis. The next step in evaluation of this product would be a reliability study on human subjects.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".