Differences between pelvic skin and bone landmark identification in different seated positions on spinal-cord injured subjects
Bibliographic record
Abstract
The purpose of this paper was to determine the differences between internal and external pelvic landmark locations in different seating positions. A computer tool developed for the registration of two series of images was used to obtain the internal geometry. First, images of the pelvis were acquired by magnetic resonance imaging (MRI) for each subject, in a supine position; internal landmarks were then identified on the images. Second, ultrasound images of the iliac crests were acquired in four seated positions. A registration algorithm was applied to obtain the transformation matrix between the two image reference systems. The MRI anatomical landmarks were, therefore, transferred into the ultrasound referential, to obtain their three-dimensional (3-D) location in the different seating positions. The external landmarks in those seated positions were identified with a 3-D digitizer. The results revealed that generally the internal and external coordinates of corresponding landmarks are statistically different. The differences are not only due to soft tissue thickness but also to different interpretations of the landmarks' locations between the supine and the seated postures. However, these differences generally did not affect significantly the accuracy with which orientation indexes can be estimated (pelvic tilt, obliquity, transverse rotation). Correlations were found between the internal and external coordinates, implying that linear regressions can be established.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".