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Enregistrement W4405313109 · doi:10.1093/rheumatology/keae622

Comment on: Changes of cerebral structure and perfusion in subtypes of systemic sclerosis: a brain magnetic resonance imaging study

2024· article· en· W4405313109 sur OpenAlexaboutno aff
Liang Fang, Zhixuan Wen, Bing Wang

Notice bibliographique

RevueLara D. Veeken · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueSystemic Sclerosis and Related Diseases
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineMagnetic resonance imagingFunctional magnetic resonance imagingMultiple sclerosisPerfusion scanningPerfusionNuclear magnetic resonancePathologyRadiologyImmunology

Résumé

récupéré en direct d'OpenAlex

Dear Editor, We would like to express our concerns and provide constructive critique regarding the recently published study on cerebral structure and perfusion changes in SSc subtypes, particularly focusing on the methodological approaches and the interpretation of results—‘Changes of cerebral structure and perfusion in subtypes of systemic sclerosis: a brain magnetic resonance imaging study’ by Tong et al. [1]. While the study offers valuable insights into the neuroimaging characteristics of SSc, certain aspects of the methodology and analysis may limit the robustness of the findings. First, the study utilizes a cross-sectional design to compare grey matter volume and cerebral blood flow (CBF) among patients with diffuse cutaneous SSc (dcSSc), limited cutaneous SSc (lcSSc), and healthy controls. A critical limitation of this approach is the inherent variability in disease duration and severity among the SSc subtypes, which could significantly impact brain structure and perfusion. The authors mention that most patients had received treatment prior to imaging, which might have alleviated some of the symptoms and potentially affected the CBF measurements. However, they do not account for the variability in treatment duration or intensity, which could confound the results. Longitudinal studies would be more appropriate to assess the progression of cerebral involvement in SSc and to account for the effects of ongoing treatment. A longitudinal design could also help to elucidate whether the observed changes in brain structure and perfusion are progressive or static, providing a clearer understanding of the disease trajectory. Second, the statistical methods used to analyze the imaging data, specifically the voxel-based morphometry and arterial spin labelling techniques, raise some concerns. The authors report significant reductions in grey matter volume in the para-hippocampal region of dcSSc patients and increased CBF in lcSSc patients. However, the use of cluster-level statistics with family-wise error correction at P < 0.01 might have masked smaller, yet clinically relevant, effects. The reliance on a stringent correction method could lead to type II errors, where true differences are not detected. A more nuanced approach, such as applying both cluster-level and voxel-wise corrections, could provide a more comprehensive understanding of the neuroanatomical changes associated with SSc. Additionally, the authors do not discuss potential biases introduced by the spatial normalization and smoothing processes inherent in voxel-based morphometry, which could distort the localization of grey matter changes. Moreover, the interpretation of increased CBF in lcSSc patients as a compensatory mechanism for microvascular dysfunction is intriguing but lacks direct evidence. The authors speculate that this increase is an adaptive response to maintain adequate perfusion despite underlying vascular pathology. However, without direct measures of vascular function or corroborative data from other imaging modalities, such as perfusion-weighted imaging or dynamic contrast-enhanced MRI, this interpretation remains speculative. For instance, a study demonstrated distinct patterns of cerebral perfusion in patients with systemic lupus erythematosus using a combination of arterial spin labelling and perfusion-weighted imaging, highlighting the need for multimodal approaches to fully understand the vascular contributions to brain changes in autoimmune diseases [2]. Incorporating such techniques could strengthen the conclusions and provide a more robust framework for understanding the cerebrovascular changes in SSc. Lastly, the discussion section includes a statement suggesting that the increased CBF observed in lcSSc patients might be protective against cognitive decline. However, this interpretation is not supported by direct cognitive assessments or longitudinal data demonstrating a correlation between CBF and cognitive outcomes over time. While the Montreal Cognitive Assessment (MoCA) scores were collected, they were not compared between SSc patients and healthy controls, nor were they correlated with specific brain regions showing CBF changes. Without these data, it is difficult to assert a protective role of increased CBF in preserving cognitive function. A more cautious interpretation would be to acknowledge the potential for increased CBF to be a compensatory mechanism, while also recognizing the need for further research to determine its impact on cognition. No new data were generated or analysed in support of this article. NATCM’s Project of High-level Construction of Key TCM Disciplines (NO: zyyzdxk-2023070). Disclosure statement: The authors have declared no conflicts of interest.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,065
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,030
Score d'incertitude au seuil0,041

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,065
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,003
Communication savante0,0030,004
Science ouverte0,0050,001
Intégrité de la recherche0,0300,029
Charge utile insuffisante (le modèle a refusé de juger)0,0050,006

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,250
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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