Meta analysis of retinal and choroidal structural changes in patients with internal carotid artery stenosis
Bibliographic record
Abstract
AIM:To systematically evaluate the changes in retinal and choroidal thickness in patients with internal carotid artery stenosis by using optical coherence tomography(OCT)through Meta-analysis.METHODS: Literatures on the measurement of retinal and choroidal structure in patients with internal carotid artery stenosis by using OCT from CNKI, VIP, WF, PubMed, the Cochrane Library, SinoMed, and Embase databases were searched for relevant studies. The retrieval time was from the establishment of the databases to January 2024. In addition, quality of the included literatures was assessed by the Newtle-Ottawa scale(NOS), and RevMan 5.4.1 and Stata 16.0 were used for statistical analysis.RESULTS: A total of 17 articles(including 18 studies)were included, and the Meta-analysis results showed that, patients with internal carotid artery stenosis had significantly thinner peripapillary retinal nerve fiber layer(pRNFL), ganglion cell complex(GCC), center macular thickness(CMT), and subfoveal choroidal thickness(SFCT)than the healthy control group(age matched normal population). The pRNFL and SFCT of the ipsilateral eye in patients with internal carotid artery stenosis become thinner compared with the contralateral eye.CONCLUSION:To a certain extent, the morphological structure of the retina and choroid can be altered by stenosis of the internal carotid artery. OCT can non-invasively detect the microstructural changes of the retina and choroid in patients with internal carotid artery stenosis, and can be used for the evaluation of internal carotid artery stenosis.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.032 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".