COVID-19: Find the Right Questions to be Answered
Notice bibliographique
Résumé
To the Editor—We read with great interest the article by Montejano and colleagues, which aimed to assess how the inclusion criteria of ongoing phase 2 and 3 clinical trials for coronavirus disease 2019 (COVID-19) treatment fit with the actual population of hospitalized individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a teaching hospital in Madrid, Spain [1]. The authors found that most of individuals admitted to their hospital do not meet the inclusion criteria for recent trials and concluded that this was mainly due to discrepancies between the ideal trial population and the actual characteristics of COVID-19 hospitalized individuals, which are rapidly evolving. Although we do agree that the characteristics of the hospitalized population change over time and might have led to the discrepancies observed, we would like to underline some inconsistencies in the methodology applied by the authors that could have led to an under estimation of eligible participants: (1) first of all the BEST [2] and DEFACOVID [3] trials, aimed to evaluate potential treatment for acute respiratory distress syndrome (ARDS). Because the authors excluded the “critically ill patients at the time of admission” from their analysed sample it is not surprising that most of the participants were not eligible for these 2 trials; (2) the author stated that “the initiation of COVID-19–specific treatment in 61% of patients” were among the most prevalent exclusion criteria. This statement would have only been true if the investigated trials would have been actively enrolling at the site when the retrospective eligibility assessment was carried out and all the individual would have had the possibility to be screened at the time of SARS-CoV-2 diagnosis/admission for the eligibility in these trials. Otherwise, considering that the observation of the authors was retrospective in nature, a prevalent user bias would have occurred and “potentially eligible individuals” at T0 (baseline) would have been wrongly identified as “non-eligible” because previously exposed to SARS-CoV-2 treatments [4]. Apart from these methodological concerns, we believe that the authors have raised an important issue. Although the COVID-19 pandemic is no longer considered a public health emergency of international concern by the World Health Organization (WHO) [5], it continues to challenge our everyday clinical practice; therefore, it remains crucial to identify residual unmet needs [6] in order to be able to pose the right causal questions [7]. In conclusion, because of the rapidly evolving clinical scenario, one important pitfall, not specifically mentioned by the authors, is that often, by the time the trial is completed, the original trial question is no longer relevant for clinical practice.
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 | 0,012 | 0,162 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,015 | 0,008 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,015 | 0,015 |
| Science ouverte | 0,004 | 0,010 |
| Intégrité de la recherche | 0,018 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,745 | 0,646 |
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 ».