Bibliographic record
Abstract
We would like to thank Chris Metcalfe for his interest in our paper and it is important for us to clarify the following issues. Firstly, our systematic overview was based on tabular data, which of course excludes individual patient data. Our conclusion regarding hospital admissions was based on the available 1447 hospitalizations due to heart failure reported in the published trials. In the Metoprolol CR/XL Randomized Intervention Trial in Congestive Heart Failure (MERIT-HF), 494 patients had at least one hospital admission and Metcalfe correctly pointed out the 768 total hospitalizations in this group due to multiple hospital admissions by individual patients 1. The report on total number of hospital days and total number of hospitalizations are rare in randomized clinical trials addressing the effect of beta blocker in heart failure. Trials often report time to first event regarding the combined endpoint of hospitalizations due to heart failure or death, which makes difficult the evaluation of individual hospital readmissions. Therefore, as in any other systematic overview, the results should be interpreted as an overall estimation of the effect of treatment on a particular disease or condition. Funck-Brentano et al. suggested that bisoprolol might be more effective in patients with lower ejection fraction or those who have non-lethal cardiovascular events 2. The notion that treatment is more effective in higher risk individuals is known, and for this particular subgroup of patients, the treatment effect derived from overall population is often underestimated (provide the treatment under question is effective). We used the best available evidence to estimate the number needed to treat (NNT). One can expect on average to avoid one hospital admission (time to event) by treating 16 heart failure patients with beta-blockers for 1 year. Second, we disagree with Metcalfe when he states, ‘an initial hospitalization may be indicative of susceptibility of further hospitalization due to disease-related or social factors’. This may happen, if no action is taken after their first hospital admission, to adequately treat or prevent heart failure. Disease-related or social factors when managed appropriately can reduce further hospitalizations 3. Third, we did not attempt to draw conclusions about the effect of treatment on individual hospitalizations nor on health economic impact of such treatment. Instead, we wrote ‘The demonstration of a reduction in hospital admission for patients with heart failure, using a simple intervention such as beta blocker administration, is likely to have an important impact on quality of life and health economics’. It seems likely that beta-blocker will influence quality of life and health economics, but a detailed discussion about this issue was out of the scope of our systematic overview. Finally, our conclusion about the effect of beta blockers on hospitalizations was derived from time to first event, simply because that is the evidence available (except in MERIT-HF). We agree with Metcalfe that data about total hospital days or total number of hospital admissions would help to evaluate more precisely the burden of heart failure.
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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 | 0.017 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.049 | 0.069 |
| Insufficient payload (model declined to judge) | 0.030 | 0.023 |
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".