Maro responds to “raising a high-pressure alarm about pediatric hypertension”
Notice bibliographique
Résumé
We thank Dr. Weiss1 for his thoughtful engagement with our article2 and particularly how to interpret the findings in the absence of additional measurement of persistent pediatric hypertension (ie, when children with initial findings of clinical hypertension or elevated blood pressure do not experience a subsequent return to normal blood pressure without antihypertensive treatment). Dr. Weiss acknowledges the well-documented underrecognition and underdiagnosis of pediatric hypertension3,-6 despite clinical practice guidelines7 that were streamlined and simplified to improve recognition. Our study2 adds to this literature but finds a clear difference in estimates between children who met the clinical requirements for pediatric hypertension based on blood pressure readings and those who received a coded diagnosis of hypertension in their electronic medical record. Additionally, those with diagnosed hypertension were uniformly less well, suggesting a diagnosis is only conferred at more advanced stages of hypertension. Why is this mismatch so important? Early identification of pediatric hypertension allows earlier intervention strategies for a disease course when much is gained by prevention. While data collected primarily for research may be ideal to design and test new strategies for potential interventions, these types of observational cohorts are few and far between. Real-world data (ie, data collected in routine care, not primarily intended for research) are relatively inexpensive to acquire and can accelerate our understanding of specific clinical areas. However, these data must be fit-for-purpose, and our comparison of clinical and billing data demonstrates meaningful gaps where the billing data do not reflect the clinical phenomenon. Dr. Weiss acknowledges similar findings in pediatric sepsis,8 and other authors have similar findings in adult sepsis9 and obesity.10 Imagine we were to attempt to design an early-stage intervention to reduce pediatric hypertension and we selected a pediatric cohort that met clinical requirements based on blood pressure readings or we selected a pediatric cohort with diagnosis codes. We would be talking about 2 pediatric populations with markedly different severity, but without studies such as ours,2 we might be unaware of the limitations in generalizability. Dr. Weiss suggests that the prevalence we estimated with clinical blood pressure readings is concerningly high and implies misclassification. We acknowledge that our study was likely to yield higher estimates compared with prior work11 based on our inability to limit to ambulatory visits. Moreover, Dr. Weiss is concerned with our treatment of multiple blood pressures recorded on the same day, which follows clinical guidelines that require the average blood pressure to be recorded.7 Dr. Weiss would have preferred that a sensitivity analysis with the lowest reading was included, and we agree that such a sensitivity analysis would have helped dispel concerns. However, clinical guidelines also require 3 distinct measure-days to declare any child hypertensive, guarding against the possibility of incorrect classification from temporary elevations either on the same day or among multiple visits. We do not believe that the magnitude of such potential misclassification changes our core findings: that clinical data yielded higher estimates of pediatric hypertension that rarely translated into administrative diagnoses of hypertension. Real-world data are not without limits. As Dr. Weiss points out, they cannot be used to evaluate measurement issues in pediatric hypertension that arise from using the incorrect blood pressure instrument or using the incorrect limb. However, they allow investigators to understand more than they would otherwise know, giving us opportunities to improve public health. We should use them with eyes wide open for both their strengths and limitations. This work was supported by the Food and Drug Administration through the Department of Health and Human Services (contract number HHSF223201400030I). None to declared.
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 | 0,003 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,017 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,005 |
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 ».