Bibliographic record
Abstract
Fracture is a leading cause of disability in the aging population. Because the cost of fracture in terms of medical expenditures and quality of life lost can be substantial, it is essential to identify a complete profile of fracture risk for the development of timely interventions. Risk factors for fracture have most often been identified clinically. Thus, the contribution by Wagner et al. in this issue of the Journal is particularly important, since it demonstrates a robust association between balance impairment and fracture in a population-based setting. It is unclear, however, whether isolating balance as a risk factor can tell us enough about the clustering of risk factors for fracture that accompanies frailty. Indeed, this problem of risk clustering is one that epidemiologists often encounter as we try to locate the mediating processes between exposures and outcomes that lead downstream through complex interacting causal pathways. In this commentary, the author discusses the importance, particularly when studying frailty and fracture, of quantifying risk clustering rather than continuing to rely on solitary risk factors. Moreover, the author suggests the use of Bayesian networks in the expansion of our tool kit in this field of research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.042 | 0.037 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".