Use of FTIR Spectroscopic Imaging to Identify Parameters Associated With Fragility Fracture
Bibliographic record
Abstract
BMD does not entirely explain an individual's risk of fracture. The purpose of this study was to assess whether specific differences in spatially resolved bone composition also contribute to fracture risk. These differences were assessed using Fourier transform infrared spectroscopic imaging (FTIRI) and analyzed through multiple logistic regression. Models were constructed to determine whether FTIRI measured parameters describing mineral content, mineral crystal size and perfection, and collagen maturity were associated with fracture. Cortical and cancellous bone were independently evaluated in iliac crest biopsies from 54 women (32 with fractures, 22 without) who had significantly different spine but not hip BMDs and ranged in age from 30 to 83 yr. The parameters that were significantly associated with fracture in the model were cortical and cancellous collagen maturity (increased with increased fracture risk), cortical mineral/matrix ratio (higher with increased fracture risk), and cancellous crystallinity (increased with increased fracture risk). As expected, because of its correlation with cortical but not cancellous bone density, hip BMD was significantly associated with fracture risk in the cortical but not the cancellous model. This research suggests that additional parameters associated with fracture risk should be targeted for therapies for osteoporosis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".