Modelling Skin Pelvic Landmark Coordinates Into Corresponding Internal Bone for Wheelchair Users
Bibliographic record
Abstract
The purpose of this study was to investigate the relationships, by linear regression, between internal and external pelvic landmarks identified by two techniques: manual digitization or skin markers. It was hypothesized that the body mass index or the skinfold thickness are significant variables in these relationships. The internal pelvic landmarks were obtained with a stereoradiographic method. Results showed that the external coordinates are generally statistically different from the internal ones; manual digitization of the landmark reduces the soft tissue artifacts compared to the use of skin markers. Different regression models were obtained according to the external acquisition method. Body mass index or skinfold thickness was generally included as a significant variable in models along the direction of the soft tissue thickness: postero-anterior direction for the anterior-superior iliac spine, medio-lateral direction for the apex of the iliac crests. With the use of skin markers, models obtained for a specific internal landmark coordinate include generally many variables, such as the other two coordinates of the landmark, body mass index, or skinfold measurements. This study presented preliminary results on the relationships between internal and external pelvic landmark coordinates. More research is needed before the full relationships are understood and adequate models are developed.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".