Bibliographic record
Abstract
SIR—We agree with Dr. Borek [1] that, despite having data only on the interaction between a single garlic preparation and saquinavir, we drew a generalized conclusion regarding the need for patients to use caution if they combine garlic supplements with saquinavir when using that drug as the sole protease inhibitor [2]. Because we have no information to suggest which constituent (or excipient) in the garlic formulation is responsible for the drug interaction, we cannot speculate about the effects of other commercial products or dietary garlic on the pharmacokinetics of saquinavir or relate our findings to allicin concentrations. We provided data on the allicin (and allin) content of the supplement we studied solely because we considered the verification of product content to be important. Since publication of our article, we have had a study brought to our attention in which garlic supplements were tested for drug release in simulated gastrointestinal conditions: most supplements released far less allicin in such conditions than they did when crushed and suspended in water [3]. Thus, the supplement we used may well release little allicin in vivo. Given the risks associated with reduced antiretroviral concentrations, we consider our conservative interpretation to be appropriate for use in advising patients [4, 5].
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.020 | 0.030 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".