Validation of vitamin D status point-of-care assessment in healthy and critically ill young children
Bibliographic record
Abstract
Vitamin D is known to have an important role in multiple organ systems.A growing body of evidence suggests there may be clinical benefits to the rapid identification and correction of vitamin D deficiency in hospitalized populations.With current standard of care practices, patient 25-hydroxyvitamin D (25-OHD) levels are generally unavailable immediately following blood collection, thereby creating a window of time during which vitamin D deficient patients remain untreated.As such, it is important to develop and validate point-of-care tests for vitamin D status assessment.The aim of this study was to determine whether point-of-care testing for 25-OHD could accurately and precisely determine vitamin D status, based on concentration and status categories.This study was conducted using stored research serum samples from recently completed projects on healthy and ill children, as well as available DEQAS samples.Qualigen® results were compared to reference methods (LC-MS/MS, NIST, etc.).With the precision verification test, samples 1 (mean 48.8 nmol/L) and 2 (mean 66.5 nmol/L) largely passed (sample 2 failed for within-laboratory for option B), whereas sample 3 (177.7 nmol/L) failed for both repeatability and within-laboratory imprecision.To improve test accuracy, sample results were averaged per run to decrease variation.Further, the Qualigen® method is suspected of having bias for higher values which, if used in clinical or research settings, may result in missed detection of deficient patients.This would need to be confirmed prior to use in clinical practice.In conclusion, these results indicate that there may be possible benefit for the use of the Qualigen® method in assessing patient vitamin D status.Although this method failed on some aspects of the precision verification test, averaging the data may be a simple practical solution to increase the accuracy in 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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".