Mistakes in terminology cause false conclusions: Vitamin D does not increase the risk of dementia
Bibliographic record
Abstract
There has been a progressive trend in recent years, to trivialize the terminology surrounding the molecules based on a secosteroid structure. The generic use of the term, "vitamin D," results in gross misrepresentations that confuse the use of a drug commonly used for patients with kidney failure, with the nutritional use of vitamin D. This commentary is a critique of one particularly bad example of that terminological trivialization. Authors may simply want to add impact to their findings when they refer to "vitamin D supplementation" when what they are reporting on is calcitriol. However, the consequences of this practice are to mislead all readers who do not go through the primary publication very carefully to understand the details behind sloppy terminology. Contrary to all the words written in the publication commented upon here, it offers no clinical evidence that vitamin D supplementation increases risk of Alzheimer's disease or dementia.
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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 | 0.044 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.018 | 0.040 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".