OCTA changes in blood flow density of patients with Alzheimer's disease: a Meta-analysis
Bibliographic record
Abstract
AIM: To explore the changes of retinal optical coherence tomography angiography(OCTA)vessel density in Patients with Alzheimer's disease(AD)by Meta-analysis, and to explore the value of OCTA in early diagnosis of AD patients.METHODS: Embase, PubMed and Cochrane Library data were searched from January 2016 to September 2021 for relevant studies on vessel density in macular area of AD patients. Two researchers independently screened the literature, extracted the data, and evaluated the risk of inclusion bias using Newcastle-Ottawa Scale(NOS). Meta-analysis was performed using RevMan 5.3 software.RESULTS: A total of 740 cases(eyes)were included in 10 literatures, including 321 cases from the AD group and 419 cases from the control group(age-matched people with normal cognitive abilities). The results of the Meta-analysis showed that the superficial vessel density in macular area of AD patients was lower than that in control group(MD=-1.58, 95%CI -2.60- -0.55, P=0.003). The deep vessel density in macular area of AD patients was lower than that in control group(MD=-2.72, 95%CI -4.36- -1.07, P=0.001). The parafoveal vessel density in AD patients was lower than that in control group(MD=-1.44, 95%CI -1.94- -0.94, P<0.00001). The avascular area in the fovea of AD patients was slightly larger than that of the control group(MD=0.05, 95%CI -0.01-0.11, P=0.13).CONCLUSION: The vessel density of each layer in macular area of AD patients were lower than that of control groups the difference was statistically significant. OCTA can assist in the early diagnosis of AD.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.045 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".