The correlation of clinical lacrimal bone density and thickness, established at the time of DCR surgery, with systemic bone mineral densitometry testing
Bibliographic record
Abstract
BACKGROUND. Due to a growing concern with regard to the relationship between osteoporosis and fractures, we wished to examine the correlation of systemic bone density with lacrimal bone characteristics (thickness and density), as measured at the time of dacryocystorhinostomy (DCR). Significant correlation would suggest that oculoplastic surgeons may screen for osteoporosis during DCR. METHODS. A prospective study of the bone mineral density in patients (n=32) undergoing DCR was conducted. During DCR, the lacrimal bone thickness and density were estimated clinically. Postoperatively, the systemic bone density was measured by dual-energy x-ray absorptiometry (DEXA) scanning. The data were analyzed using Student's t-test, Pearson correlation and Pearson chi-square methods. RESULTS. Analyzed in a bivariate arrangement, significant correlation (p<0.05) was detected between the systemic bone density (as measured at two sites, the femoral head and lumbar spine) and the lacrimal bone characteristics (thickness and density). Therefore, the lower the lacrimal bone thickness or density, the lower the systemic bone density. INTERPRETATION. With the finding of significant correlation between lacrimal bone thickness and density and systemic bone density, oculoplastic surgeons can screen for osteoporosis during DCR. If low-density thin bone is encountered during DCR, the patient's general practitioner should be alerted.
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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 | 0.004 |
| 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".