Bibliographic record
Abstract
We are grateful for the comments by Prof. Klevay and we agree that copper supplementation in a population of elderly heart failure patients could have significant benefits. Our choice of agents and daily intakes in the study1 were generally made on the basis of previous animal and human work.2 However, although there are data on the prevalence and potential effects of copper deficiency,3 there are few about supplemental doses in humans at risk of deficiency.4 We were therefore cautious, choosing 1.2 mg per day based on recommended daily intakes. The recent MAVIS study has demonstrated that multiple micronutrient supplementation is not of benefit in reducing morbidity from infections in otherwise well ambulatory elderly patients.5 However, patients with long-term multi-system illnesses, such as chronic heart failure (CHF), might be more likely to have important relative deficiencies in multiple micronutrients due to reduced intake, increased degradation because of metabolic stress, and increased excretion.6 In such patients, single agent supplementation might be ineffective or exacerbate deficiencies elsewhere with no overall change in status. Furthermore, the potential benefits of micronutrient supplementation in CHF given high re-admissions rates, poor overall quality of life, and persistent symptoms are significant.2
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.025 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 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".