Neonatal Brain Volumetric Studies: Regression Analysis and Interpretation
Notice bibliographique
Résumé
To the Editor.—Volumetric techniques are increasingly used to investigate the impact of injury or intervention on the neonatal brain. Parikh et al1 recently reported in an interesting article that postnatal dexamethasone therapy is followed by reduced cerebral tissue volumes. In view of relatively poor MRI signal contrast between gray and white matter in neonates, researchers overcome challenges in regard to image acquisition and segmentation techniques. However, there remains a big challenge in how best to analyze and interpret the volumes measured. When interpreting the results of neonatal brain volumetric studies, there is often a need to address possible confounding factors. The decision on what confounding factors/covariates to include in the regression model is critical for the conclusions of the study. Equally critical is the way that these possible confounding factors are being tested for and analyzed in the regression model.With regard to the hypothesis tested, previous scientific evidence is usually a good way to start finding which possible covariates to test for. In a landmark article in 1998, Hüppi et al2 addressed clinical parameters that correlate with neonatal cerebral volumes. Moreover, depending on the selection criteria and hypotheses, different samples may display different significant covariates. The statistical significance of group differences on demographic and clinical characteristics may be of some help but is limited. Factors not significantly different between groups may well be proven statistically significant when entered in the regression model to test the primary hypothesis, and vice versa. Also, the influence of a covariate on the primary outcome measure may partially overlap with the influence of a different covariate, which makes the combination tested crucial.In regard to the regression model, it is a good exploratory approach to enter and test the possible covariates initially 1 by 1. This can be followed by a stepwise regression that involves the more statistically significant ones. It is safer to use larger P values (eg, .1 or .2) for entry criteria in the stepwise regression than for exit (eg, .05 or .1). The covariates that have the most influence on the primary outcome (cumulative effect if >1) should stay in the model, and then the primary outcome measurements can be adjusted accordingly. It is important to note whether comparisons for the primary outcome were statistically significant before and after the regression analysis.With respect to the above and in terms of cortical volume, along with postmenstrual age at scan, scaling effects and size differences between subjects and groups at scan could have an influence on neonatal cerebral volumes and be a significant covariate. Also, uncomplicated germinal matrix-intraventricular hemorrhage could be a significant covariate for the cortical volume.3 For the reader, understanding is benefited from adequate details on the demographic and clinical characteristics of the subjects. The presentation of the raw data and their statistics, before adjustments, is also essential for the interpretation of neonatal brain volume studies.
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 | 0,024 | 0,209 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,005 | 0,001 |
| Intégrité de la recherche | 0,005 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,004 |
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 ».