Developing a Bone Mineral Density Distribution Model to Reduce the Risk for Postoperative Hip Surgery Complications in Racial Minorities: A Research Protocol
Bibliographic record
Abstract
Introduction: Racial minorities, including Black and Hispanic populations, suffer more postoperative hip surgery complications relating to fixations and replacements than White populations. The goal is to use CT scans and 3D projections to create a bone mineral density distribution model for these racial groups. Methods: A preliminary trial of the proposed methods was conducted to ensure reliable data could be obtained. Semi-automatic segmentation of left femurs from decedents was done in 3D Slicer, followed by mean bone mineral density analysis. Discussion: Preliminary trials show that the BMD processing pipeline gives viable results for sample groups of 5 CT scans. Future studies done with this research protocol will involve a larger sample size and the inclusion of machine learning extensions that will reduce the processing time of the CT scans. Confounding variables not considered in the preliminary trial will also be analyzed. Conclusion: The use of the streamlined pipeline in conjunction with other imaging software could provide an alternative to bone mineral density imaging, as well as lead to the development of models for minorities with less representation in medical data.
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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 | 0.035 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".