A patient’s informative mistake: niacin is very effective in correcting dyslipidaemia: Table 1
Bibliographic record
Abstract
A 72-year-old man at high risk for cardiovascular disease, with a history of peripheral vascular disease and type 2 diabetes, presented with lipids above targets despite maximum daily treatment with atorvastatin 80 mg, fenofibrate supra 160 mg daily, and ezetimibe 10 mg. His low density lipoprotein cholesterol (LDL-C) was 2.6 mmol/l, total cholesterol: HDL ratio 5.6, and high density lipoprotein cholesterol (HDL-C) 0.9 mmol/l. Because his lipids were not within target, he was advised to start 2250 mg of niacin in three divided doses daily. For 5 months, he mistakenly took 2250 mg of niacin three times daily, a consumption of 6750 mg/day! The effects on his lipids were: HDL-C increased nearly 100% to 1.7 mmol/l, LDL-C decreased by 50% to 1.3 mmol/l, and cholesterol: HDL ratio decreased by over 50% to 2.1. His excessive intake dramatically demonstrates the positive effect of niacin on lipids. Fortunately he did not suffer adverse effects from taking more than the recommended limit of 3000 mg/day.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".