Association between calcium and vitamin D in geriatric patients hospitalized for Covid-19: Results from the GERIA-COVID study
Bibliographic record
Abstract
ABSTRACT The deficiency of 25OH vitamin D (25[OH]D) is common in the older population. It physiologically triggers secondary hyperparathyroidism resulting in normal circulating calcium levels. Adjusted calcium (CaA) is estimated by the PAYNE method and several studies report a misclassification of calcium status by corrected calcium compared to ionized calcium (CaI) in older patients. Hypocalcemia is common in older COVID-19 patients. Blunted secondary hyperparathyroidism explain this high prevalence of hypocalcemia in COVID-19. However, no studies have focused on patients older than 75 years despite the high mortality rate in this population. In the present study, the association between the different types of calcium (CaI, CaA, and total calcium [CaT]) and 25(OH)D deficiency (below 50 nmol/L) was investigated. The study of the correlation between each type of calcium was performed secondarily. Observational monocentric study focused on the GERIA-COVID database during the second wave of COVID-19 in France from October 2020 to March 2021. COVID-19 was diagnosed with RT-PCR and/or chest CT-scan. A population of 181 older COVID-19 patients (86.4 years ± 5.7) was analyzed. Sixty-three patients (34.8%) were deficient in 25(OH)D. The prevalence of total and ionized hypocalcemia was 44.1% and 39.2%, respectively. A negative association was reported in linear regression between 25(OH)D deficiency and CaA (β =-0.052 [- 0.093; -0.010], p = 0.015) as well as with CaT (β = -0.05 [-0.09; -0.01], p =0.034) in the multivariate model. No association was found between vitamin D deficiency and CaI. In the multivariate models, there was no association between each type of calcium and PTH. CaI was correlated with CaT (r = 0.39, p < 0.001) and with CaA (r = 0.15, p = 0.043). Secondary hyperparathyroidism was not activated in the context of COVID-19 in this study. After reviewing the literature, this appears to be the first study in older patients to expose such results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".