Reply to: The Association Between Cognitive Decline and Bone Loss and Fracture Risk Is Not Affected by Medication With Anticholinergic Effect
Bibliographic record
Abstract
To the Editors: We are grateful to Dr. Naharci for the interest in our study reporting the association between cognitive decline and bone loss and fracture risk. (1) We agree that bisphosphonates (BPs) have a proven effect on reducing bone loss and fracture risk. (2) Medication with anticholinergic (ACH) side effects may also affect cogni-tive function as well as propensity to fall and fracture, although these effects have not been demonstrated in all studies. (3) We did not include these medication classes in our models because in observational studies the relationship between medication and outcomes is likely driven by factors associated with medication use. This bias by indication can only be avoided by specific study design (ie, propensity score matching), which was beyond the scope of our study. (4) However, we have conducted additional analyses to determine the prevalence of BP and ACH medication in our cohort, the association between these medication classes and our study outcomes and the impact of the addition of these medication classes to our findings. BP and ACH use were self-reported and obtained by questionnaire at baseline, and years 5 and 10. Bone mineral density (BMD) was assessed by dual-energy X-ray absorptiometry (DXA) and cognitive function using the Mini Mental State Examination (MMSE) test during all clinical visits. Follow-up time for BMD
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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 | 0.006 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.019 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".