Efficiency of the Vitamin D Status Diagnosticator amongst Geriatric Patients with COVID-19
Bibliographic record
Abstract
The vitamin D status diagnosticator (VDSD), a 16-item tool, effectively identifies hypovitaminosis D in healthy older adults and can assist in determining the need for blood tests in this population. Assessing vitamin D levels is particularly crucial in the context of COVID-19. This study aimed to evaluate the VDSD's effectiveness in pinpointing hypovitaminosis D in older adults affected by COVID-19. The research involved 102 unsupplemented geriatric inpatients consecutively admitted to the acute geriatric division of Angers University Hospital, France, with an average age of 85.0 ± 5.9 years (47.1% women). The physician-administered VDSD was conducted simultaneously with the measurement of serum 25-hydroxyvitamin D (25(OH)D). Hypovitaminosis D was defined as a serum 25(OH)D concentration of ≤75 nmol/L for vitamin D insufficiency and ≤50 nmol/L for vitamin D deficiency. Results revealed that 87 participants (85.3%) had vitamin D insufficiency and 63 (61.8%) had vitamin D deficiency. The VDSD accurately identified vitamin D deficiency with an area under the curve (AUC) of 0.81 and an odds ratio (OR) of 40. However, its accuracy in identifying vitamin D insufficiency was lower (AUC = 0.57). In conclusion, the 16-item VDSD, a concise questionnaire, effectively identifies vitamin D deficiency in geriatric patients with COVID-19. This tool can be valuable in guiding the decision to administer vitamin D supplementation during the early stages of COVID-19.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".