Raman Spectroscopic Analysis of Fingernail Clippings Can Help Differentiate between Postmenopausal Women who Have and Have Not Suffered a Fracture
Bibliographic record
Abstract
Raman spectroscopy was applied to nail clippings from 633 postmenopausal British and Irish women, from six clinical sites, of whom 42% had experienced a fragility fracture. The objective was to build a prediction algorithm for fracture using data from four sites (known as the calibration set) and test its performance using data from the other two sites (known as the validation set). Results from the validation set showed that a novel algorithm, combining spectroscopy data with clinical data, provided area under the curve (AUC) of 74% compared to an AUC of 60% from a reduced QFracture score (a clinically accepted risk calculator) and 61% from the dual-energy X-ray absorptiometry T-score, which is in current use for the diagnosis of osteoporosis. Raman spectroscopy should be investigated further as a noninvasive tool for the early detection of enhanced risk of fragility fracture.
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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 | 0.002 |
| 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) | 0.001 | 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".