An Anatomic Study of Coronoid Cartilage Thickness with Special Reference to Fractures
Bibliographic record
Abstract
Current classification systems of coronoid fractures are based on plain radiographic and/or CT measurements where cartilage is radiolucent and therefore not taken into account. Hence, during surgery, coronoid fracture fragments are much larger than anticipated for when viewed on CT. The main goal of this research is to determine the ulnar coronoid cartilage thickness in order to aid surgeons and clinicians in the proper classification and operation of coronoid bone fractures. Thirty ulnas have been dissected from embalmed cadaveric upper extremities and placed into a 30% diluted hypaque cartilage contrasting agent prior to CT. Using OsiriX (3.6–64 bit), 3‐D models for each ulna were constructed. Using the OsiriX point tool, 12 identifiable landmarks were placed onto the ulnar coronoid process, and then translated onto 2D orientation slices. This serves the purpose of measuring cartilage thickness from coronal, sagittal, and axial coronoid CT slices. Knowing the cartilage thickness will not only allow for proper classification of ulnar coronoid fractures by surgeons/clinicians, but will also allow for the accurate engineering of coronoid head prostheses in cases of irreparable coronoid process fractures. Moreover, discrepancies between biomechanical/anatomical studies and clinical outcome studies could then be resolved. Grant Funding Source : Internal
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".