Statins and <i>Clostridum difficile</i> : a clinically relevant interaction?
Bibliographic record
Abstract
Statins are a class of lipid-lowering drugs which decrease cholesterol synthesis and have beneficial effects on cardiovascular disease. Observational studies have ascribed many other beneficial effects of statins, including reduced risk of infections, cancer and Alzheimer's.1 The authors argue that these myriad beneficial observations are plausible as ‘pleiotropic’ or multiple effects of statins including anti-inflammatory, antioxidant, immunomodulatory, antiapoptotic, antiproliferative, antithrombotic, antimicrobial and endothelium-protecting properties2 have been demonstrated in the laboratory. However, are these beneficial effects too good to be true? It has been suggested that many of the apparent observed beneficial effects of statins can be explained by a ‘healthy user effect’. Statin users, especially compliant users, have been shown to be more likely to be insured, live at home, to have stopped smoking and to be more likely to engage in other positive health behaviours such as undergoing cancer screening and being vaccinated.3 4 Studies using administrative databases often are unable to adjust for these variables, and three studies on patients with pneumonia4–6 which adjusted for some of these variables found no decreased risk of pneumonia associated with current use of statins, and even reported a tendency towards increased risk. Motzkus-Feagans and colleagues7 …
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".