Accuracy of digitization of bony landmarks for measuring change in scapular attitude
Bibliographic record
Abstract
Digitizing bony landmarks is a common technique used to measure scapular position, but it has not been validated against a gold standard. The aim of this study was to determine the accuracy of this technique for four physiological arm movements using optoelectronic markers mounted on scapular bone pins as a gold standard. Eight subjects had bone pins inserted into their lateral scapular spine. Three points were digitized on the scapula with an optoelectronic probe: the medial root of the scapular spine, the posterolateral corner of the acromion, and the inferior angle of the scapula. The four active movements tested in this study were glenohumeral abduction, glenohumeral horizontal adduction, hand behind back, and forward reaching. The three bony landmarks were digitized six times in three different positions for each movement. Data from one subject were rejected secondary to pin loosening. The overall position-specific r.m.s. errors ranged from 2.0 degrees to 12.5 degrees. The full abduction position had considerably higher r.m.s. errors than the other positions (posterior tipping, 12.5 degrees; upward rotation, 7.3 degrees; internal rotation, 12.0 degrees). It appears that the digitization of bony landmarks may be a valid method for measuring changes in scapular attitude with the following caveats: the full abduction position has a high r.m.s. error, and small scapular motions have high percentage errors.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".