Retinal changes in patients with mild cognitive impairment: An optical coherence tomography study
Bibliographic record
Abstract
Background: Optical coherence tomography (OCT) is a noninvasive method of analyzing in vivo retinal architecture. It also measures retinal nerve fiber layer (RNFL) thickness, which is useful in managing diseases of the retina. Age-related thinning of the retinal ganglion cell complex has been measured using OCT. The present study is to evaluate the RNFL and ganglion cell layer (GCL) thickness using spectral domain OCT in patients with cognitive impairment (CI) and to study the correlation between RNFL and mini–mental state examination (MMSE) scores. Materials and Methods: A case–control study was done on 88 eyes of 44 patients, of which 27 belong to mild CI (MCI) and 17 were controls. They were assessed using MMSE/MINICOG/Montreal Cognitive Assessment tests and retinal OCT for RNFL, GCL, and inner plexiform layer (GCL + IPL) analysis. Results: RNFL thickness was reduced in all quadrants, more in superior and inferior quadrants in patients with MCI. GCL + IPL layer showed overall thinning in all quadrants, of which inferonasal and inferior quadrants were thinnest. Conclusion: MCI patients were prone to develop neurodegeneration even in the absence of microvascular changes in the retina. Hence, it is suggested to carry out routine evaluation of retina with OCT in all patients above the age of 60 to detect early neurodegenerative changes for early management. It is also noted that the sensitivity of GC + IPL was higher than that of RNFL to discriminate MCI from controls.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".