MétaCan
Menu
Back to cohort
Record W1514330798

Investigation of vitamin and mineral tablets and capsules on the Canadian market.

2006· article· en· W1514330798 on OpenAlexaffabout
Raimar Löbenberg, Wayne Steinke

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Quality and Counterfeiting
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVitaminFood scienceBusinessMedicineChemistryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.248
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2006
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicPharmaceutical Quality and CounterfeitingFrench-language works237,207