Response
Notice bibliographique
Résumé
In response to Ding's comments regarding the age-dependent curve fitting, we agree that a biexponential might be better suited to fit T2 data extending into the second decade of life. However, our age range was limited to 4 years, 5 months and as clearly stated in the text, “…insufficient data from older subjects may impede the detection of a second exponential term.” In addition, it is clear from Table 2 that in some regions of interest the biexponential did indeed slightly increase the quality of fit (adjusted R2 value). However, because the fitting failed to converge in the two regions of the corpus callosum and the thalamus, for the sake of consistency we chose to use a monoexponential fit. We also believe that different regions of interest might exhibit different age-dependent behaviors, as shown by the diffusion tensor imaging study referred to by Ding, where the genu of the corpus callosum showed a mono- rather than a biexponential decay with age (1). Another point to consider is the significant variability in T2 values for very young subjects, particularly in frontal white matter. This variability, combined with a generally low number of subjects for this age group, can limit the detection of subtle features in the fits. In any case, these are empirical fitting attempts with no theoretical underpinnings. Many other mathematical functions could have fitted the data well. These are purely descriptive functions and no theory about development depends on their validity. With regard to the acquisition method, it is well known that there are multiple components of T2 and that a two-echo calculation of T2 has limitations, in particular when done with a multislice sequence on a clinical scanner. It is unclear whether a triple-echo sequence provides significantly more accurate T2 estimates than two dual-echo sequences. In any event, because of the extensive protocol of the full study (2), the time limitations often did not even permit the second dual-echo data to be acquired. We also stress that these acquisitions, in fact, only provide pseudo-T2 values since they are based on fast spin-echo sequences. The important point is that these pseudo-T2 measurements are reproducible and reflect developmental changes in brain tissue. We believe these acquisition differences would not significantly affect the overall behavior of estimated T2 with age, particularly when considering the large change in T2 values for the age range considered. Ilana R. Leppert MEng*, C. Robert Almli PhD , Robert C. McKinstry MD, PhD , Robert V. Mulkern PhD?, Carlo Pierpaoli MD,PhD?, Micheal J. Rivkin MD**, G. Bruce Pike PhD , * Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada, Developmental Neuropsychobiology Laboratory, Departments of Neurology, Psychology, Programs in Neuroscience, Occupational Therapy, Washington University School of Medicine, St. Louis. Missouri, USA, Mallinckrodt Institute of Radiology and, St. Louis Children's Hospital, Washington University Medical Center, St. Louis. Missouri, USA, ? Department of Radiology, Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA, ? National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA, ** Department of Neurology, Psychiatry and Radiology, Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA, Montreal Neurological Institute McGill University Montreal, Quebec, Canada.
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 | 0,040 |
| 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 | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,014 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,158 | 0,091 |
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 ».