Investigation of vitamin and mineral tablets and capsules on the Canadian market.
Bibliographic record
Abstract
PURPOSE: The goal of this study was to investigate the disintegrating properties of tablets and capsules containing minerals and vitamins commercially available on the Canadian market and to review their label information. METHODS: The labels were examined for product-related information. The first disintegration test stage was performed using Simulated Intestinal Fluid (SIF) pH 6.8 for 20 minutes. Products which did not disintegrate were further investigated using USP disintegration conditions for dietary supplements. RESULTS: The provided label information is difficult to understand and in some cases pseudo-scientific. Thirty out of thirty-nine tablets and six out of ten capsules had a Drug Identification Number (DIN). Twenty-one of thirty-nine tablets and four out of the ten capsules did not disintegrate within 20 minutes. Using the USP disintegration conditions for dietary supplements nine tablet products did not fully disintegrate but all capsules passed the test. None of the three "time-released" products disintegrated under the applied conditions. CONCLUSIONS: Industry should follow already existing label recommendations more closely to allow the consumers to make an informed decision on their products by providing only essential information rather than using pseudo-scientific terms. The results of the disintegration study indicated that disintegration, one of the most basic quality control parameters, is still a concern for dietary supplements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".