Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,136 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,007 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,007 | 0,005 |
| Intégrité de la recherche | 0,049 | 0,069 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,030 | 0,023 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».