Better Screening Value of Sylvian Fissure Ratio on Cognitive Decline Among Female Compared to Male: An Observational Study in Elderly Patients With Cerebral Small Vessel Disease in Soochow
Bibliographic record
Abstract
Background: Cognitive decline (CD) occurs frequently in elderly patients with cerebral small vessel disease (CSVD). In China, elderly patients are more likely to enter healthcare in community hospitals where no magnetic resonance imaging (MRI) is available. This study aimed to explore the screening value of Sylvian fissure ratio (SFR) on CD and compare its gender difference from community-transferred patients. Methods: We performed a single-center, observational study (collected between April 1, 2016, and March 1, 2019) to evaluate the association between Montreal Cognitive Assessment (MoCA) and SFR in 203 eligible community-transferred patients. Baseline characteristics of patients were collected during hospitalization. Multiple linear regression analyses were used to estimate the effect of variables on MoCA, and interactions between select variables were analyzed in different models. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative effect of SFR to severe CD. Results: We identified that a meaningful SFR cutoff of 0.05 had important screening value (likelihood ratio test, p = 0.067) on CD. The ratio had a lower screen value in males when compared to females (adjusted β, −5.54; 95% CI, −8.78 to −2.30 vs. adjusted β, −1.01; 95% CI, −2.84 to 0.82). The gender difference was further verified by ROC curve analysis, in which this discriminative effect was more potent in females (from 0.878 to 0.948) compared to males (from 0.838 to 0.837). Conclusion: An SFR of 0.05 may be more useful to distinguish CD in female patients with CSVD than male patients in whom the syndrome is suspected clinically.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".