Clinical identification of older adults with hypovitaminosis D: Feasibility, acceptability and accuracy of the ‘Vitamin D Status Diagnosticator’ in primary care
Bibliographic record
Abstract
The 16-item Vitamin D Status Diagnosticator (VDSD) tool was built to diagnose, without resorting to a blood test, hypovitaminosis D among healthy seniors living at home. The objective of this study was to determine the feasibility of the VDSD by general practitioners (GPs), the acceptability to outpatients, and the diagnostic accuracy of the VDSD in primary care. Ten French GPs were asked from March to May 2015 to perform the VDSD in 30 consecutive outpatients aged ≥70years, living at home, presenting with a history of recurrent falls and/or osteomalacia, and taking no vitamin D supplements. Feasibility was defined as a proportion >70% of VDSD forms fully completed. Completing time, acceptance rate and, when applicable, the reasons for non-completing were assessed, together with the metrological properties of the VDSD to identify hypovitaminosis D ≤75nmol/L, or ≤50nmol/L or ≤25nmol/L. Of the 242 enrolled patients, 218 (mean, 79 ± 6years; 46.3% women) received a VDSD, i.e. completing rate of 90.1%, with an average completing time of 1 min and 48s. The acceptance rate by the patients was 98.8%, and all GPs were satisfied with the tool. The VDSD identified hypovitaminosis D≤75nmol/L with an accuracy of 84.7%, hypovitaminosis D≤50nmol/L with accuracy 75.4%, and hypovitaminosis D≤25nmol/L with accuracy 71.0% (n = 183 assays). The 16-item VDSD can be considered as feasible, acceptable and accurate for diagnosing hypovitaminosis D among older outpatients in primary care without resorting to an expensive blood test.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".