Analysis of the characteristic changes of macular thickness in patients with Parkinson′s disease
Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".