Regional apparent density correlations within the proximal humerus
Bibliographic record
Abstract
BACKGROUND: Bone quality influences humeral implant selection for shoulder arthroplasty. However, little is known about how well bone near the humeral resection represents more distal cancellous bone. This investigation aimed to quantify the correlations between the apparent density of sites near the humeral head resection plane and cancellous sites throughout the metaphysis. METHODS: Using computed tomography data from 98 subjects, apparent bone density was quantified in 65 regions throughout the proximal humerus. Pearson's correlation coefficient was determined comparing the density between samples from the humeral resection and all supporting regions beneath the resection. Mean correlation coefficients were compared for (i) each sample region with all support regions, (ii) pooling all sample regions within a slice, and (iii) considering sample regions correlated with only the support regions in the same anatomic section. RESULTS: Stronger correlations existed for bone sampled beneath the resection (0.33 ± 0.10≤ r ≤ 0.88 ± 0.10), instead of from the resected humeral head (0.22 ± 0.10≤ r ≤ 0.66 ± 0.14). None of sample region correlated strongly with all support regions; however, strong correlations existed when sample and support regions both came from the same anatomic section. DISCUSSION: Assessments of cancellous bone quality in the proximal humerus should be made beneath the humeral resection not in the resected humeral head; and each anatomic quadrant should be assessed independently.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".