Heteroscedastic Regression Analysis of Factors Affecting BMD Monitoring
Bibliographic record
Abstract
Identifying factors affecting BMD precision and interindividual heterogeneity in BMD change can help optimize BMD monitoring. BMD change for the lumbar spine and total hip for short-term reproducibility (n = 328) and long-term clinical monitoring (n = 2720) populations were analyzed with heteroscedastic regression using linear prediction for mean (monitoring population only) and log-linear prediction for SD (both populations). For clinical monitoring, male sex, baseline body mass index (BMI), and systemic corticosteroid use were associated with greater SD of BMD change. Weight gain was negatively associated with SD for the hip, whereas height change was positively associated with SD for the spine. Each additional year of monitoring increased the SD by 6.5-9.2%. Osteoporosis treatment affected mean change but did not increase dispersion. For short-term reproducibility, performing scans on a different day increased the SD of measurement error by 38-44%. Baseline BMD, difference in bone area, and a repeat scan performed by different technologists were associated with higher measurement error only for the hip. For both samples, heteroscedastic regression outperformed models that assumed homogeneous variance. Heteroscedastic regression techniques are powerful yet underused tools in analyzing longitudinal BMD data and can be used to generate individualized predictions of BMD change and measurement error.
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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 | 0.046 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".