Bibliographic record
Abstract
We thank Sugiyama (1) for the interest in our work (2). We agree that the finding of no association between serum 25-hydroxyvitamin D (25(OH)D) concentration and odds of fracture in young children (2) was consistent with a previous case-control study in older children (3) and prospective studies of 25(OH)D during pregnancy (4, 5) and the neonatal period (6). As suggested by Sugiyama, it is possible that there is a compensatory mechanism whereby low vitamin D status leads to increased bone mineralization due to an increase in bone strain (7, 8). Unfortunately we were not able to evaluate this hypothesis in our study because bone mass and mechanical strain were not measured. Future studies with additional bone measures would be needed to evaluate this hypothesis. As Sugiyama has indicated, the null finding for 25(OH)D conflicts with our results of an inverse association between vitamin D supplement use and odds of fracture. 25(OH)D is generally regarded as the preferred biomarker of current vitamin D status, but it is not without limitations (9). Further, serum 25(OH)D reflects only current vitamin D status, not long-term intake (10). It is possible that our measure of the use of vitamin D supplements reflects an earlier or prolonged period of exposure that may be more important for fracture risk than current vitamin D status. It is also possible that our finding for vitamin D supplement use was the result residual confounding despite our attempt to control for known confounders.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.033 | 0.038 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".