Profiling older community-dwellers with hypovitaminosis D: A classification tree analysis
Bibliographic record
Abstract
Despite the high prevalence of hypovitaminosis D in older adults, universal vitamin D supplementation is not recommended due to potential risk of intoxication. Our aim here was to determine the clinical profiles of older community-dwellers with hypovitaminosis D. The perspective is to build novel strategies to screen for and supplement those with hypovitaminosis D. A classification tree (CHAID analysis) was performed on multiple datasets standardizedly collected from 1991 older French community-dwelling volunteers ≥ 65 years in 2009-2012. Hypovitaminosis D was defined as serum 25-hydroxyvitamin D ≤ 50 nmol/L. CHAID analysis retained 5 clinical profiles of older community-dwellers with different risks of hypovitaminosis D up to 87.3%, based on various combinations of the following characteristics: polymorbidity, obesity, sadness and gait disorders. For instance, the probability of hypovitaminosis D was 1.42-fold higher [95CI: 1.27-1.59] for those with polymorbidity and gait disorders compared to those with no polymorbidity, no obesity and no sadness. In conclusion, these easily-recordable measures may be used in clinical routine to identify older community-dwellers for whom vitamin D supplementation should be initiated.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".