Response
Bibliographic record
Abstract
Dr Grant states that there is strong and sufficient evidence that vitamin D supplementation will reduce cancer risk. Although we acknowledge that there are potential health benefits associated with vitamin D supplements, and we respect the Hill criteria, we remain skeptical. A more thorough review of the literature than that conducted by Dr Grant shows that the evidence is mixed. Our conclusion is in agreement with the Institute of Medicine, which states, “Although data related to cancer risk and vitamin D are potentially of interest, a relationship between cancer incidence and vitamin D (or calcium) nutriture is not adequately and causally demonstrated at present; indeed, for some cancers, there appears to be an increase in incidence associated with higher serum 25-hydroxyvitamin D (25OHD) concentrations or higher vitamin D intake” (1). As noted in our Commentary, over the past 20 years, clinical trials have provided repeated examples of promising nutritional interventions that were ultimately shown to be ineffective and sometimes harmful. Until clinical trial data support the observational findings regarding vitamin D, it is premature to draw firm conclusions regarding the balance between benefits and harm from vitamin D supplementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.033 | 0.024 |
| Insufficient payload (model declined to judge) | 0.085 | 0.042 |
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".