Analysis of the characteristic changes of macular thickness in patients with Parkinson′s disease
Notice bibliographique
Résumé
Objective To analyze the characteristic changes of macular thickness in patients with Parkinson′s disease by spectral-domain optical coherence tomography (SD-OCT), and find out the association between macular thickness and disease progression, cognitive dysfunction, visuospatial impairment and asymmetry of motor symptoms. Methods Seventy-one Parkinson′s disease (PD) patients who were admitted to the Department of Neurology, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2016 to May 2018 and sixty-one healthy controls who volunteered to participate for the same period were enrolled and underwent SD-OCT examination. The macular thickness of all retinal quadrant segments, foveal thickness, and macular volume between the two groups were comparatively analyzed. Associations between macular measurements and clinical parameters such as disease duration, Unified Parkinson′s Disease Rating Scale part Ⅲ (UPDRS-Ⅲ) scores, Montreal Cognitive Assessment (MoCA) total scores, and visuospatial subscores were analyzed using generalized estimated equation fitted with linear regression models. Results Mean macular thickness in the PD group was significantly reduced compared with those in the control group ((261.94±12.90) μm vs (270.96±10.71) μm, B=-8.135, P<0.01). All quadrants of macular thickness (except fovea and 1 mm central zone) in the PD group were reduced compared with those in the control group. Receiver operating characteristic (ROC) curve analysis revealed that inner superior thickness could predict the presence of PD with an area under ROC of 0.727 (95%CI 0.662-0.792, P<0.01). UPDRS-Ⅲscores were negatively correlated with foveal thickness (B=-9.132, P=0.034), 1 mm central zone thickness (B=6.963, P=0.036) and all quadrants of the inner ring (superior (B=-7.727, P<0.01), inferior (B=-5.169, P=0.044), nasal (B=-5.960, P<0.01) and temporal (B=-5.905,P<0.01)) macular thickness. The disease duration had no relationship with any quadrant of macular measurements. No statistically significant difference was found between the macula parameters of the hemiretinae corresponding to more and less severely affected cerebral hemisphere. MoCA total scores were positively correlated with all quadrants of the inner ring (superior (B=2.693, P=0.007), inferior (B=3.391, P=0.002), nasal (B=2.609, P=0.001) and temporal (B=2.115, P=0.013)) macular thickness. MoCA visuospatial subscores were positively associated with average macular thickness (B=4.368, P=0.042), macular volume (B=0.161, P=0.004), inferior (B=8.582, 6.541), nasal (B=8.130, 6.017) and temporal (B=5.938, 5.316) quadrants of outer and inner rings macular thickness (all P<0.05). Conclusions In PD patients, the macular thickness and macular volume were decreased. Asymmetry was not identified between hemiretinae in PD. Some quadrants of macular thickness were associated with disease progression, cognitive dysfunction, and visuospatial impairment. Key words: Parkinson disease; Optical coherence tomography; Macular retina; Asymmetry; Visuospatial impairment
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 ».