Response to letter regarding “Assessing the association between preoperative neurocognitive status and IDH1 mutation status in high-grade gliomas: A deeper look into potential confounding variables.”
Notice bibliographique
Résumé
Thank you for the opportunity to respond to the letter from Dr. Waqar Khan regarding our paper titled “Assessing the association between preoperative neurocognitive status and IDH1 mutation status in high-grade gliomas.”1 Our study indicates that IDH1—wild-type HGG patients present greater neurocognitive impairment, in executive functions particularly, compared to IDH1-mutant ones. After adjusting for well-known factors, we support that tumor growth kinetics may play a more profound role than other parameters in neurocognitive status of HGG patients. We appreciate authors’ interest in our study, especially with regard to their suggestion for a deeper look into potential confounding variables. Firstly, Dr. Aimen Wagar Khan highlights the need for a larger sample size and for a multi-centered study design for more reliable and generalizable results. We agree that bigger studies are more powerful by providing a large sample size that allows generalizability. Nevertheless, there are occasions that while results of local single-center studies support a hypothesis, larger multi-centered studies fail to do so questioning thus if the bigger is always the better.2 Multi-centered design may come with disadvantages such as heterogeneity in methods and patients’ characteristics. Our study design limited inter-rater reliability bias as diagnostic/intervention techniques were all performed by one medical team of each specialty. In parallel, our sample (n = 147) is similar to the ones employed by consistent with our results studies.3 Conclusively, without aiming to compromise the value of multi-centered studies, we would like to highlight that single-center studies are also of great importance when they are well designed. Secondly, authors point out as a potential confounder the fact that most of our patients were on corticosteroids at the time of cognitive assessment. In particular, they make a reference to a study4 showing, according to Dr. Wagar Khan, that diminished executive functions are linked to corticosteroids in patients with recurrent glioblastoma. However, going through a thorough reading of the aforementioned study, one would notice that only memory and not executive functions (our cognitive measurement) were significantly associated with corticosteroid intake. In general, most data on corticosteroids’ adverse cognitive effects comes from patients with systematic conditions as opposed to brain tumors. Regarding gliomas, there are only few studies with contradicting results on corticosteroids’ effect on cognition.5 Finally, the fact that the majority of our patients in each subgroup were on corticosteroids does not enable us to hypothesize that our differences in cognitive status between IDH-wild type and mutant one patient were due to steroids. Finally, authors point out as a confounding factor potential vascular comorbidities/diabetes mellitus patients may have suffered from. Indeed, vascular disorders such as diabetes mellitus may are associated with poor cognitive function in general—older than our clinical sample—population, however.6 Regarding gliomas, meta-analyses support that patients with diabetes mellitus present a decreased risk for developing gliomas7 indicating that glioma cohorts may have decreased risk for diabetes mellitus. Nevertheless, further studies are warranted to explore the effect of vascular comorbidities on cognition in glioma patients.
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 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,014 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,038 | 0,027 |
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 ».