OPTICAL COHERENCE TOMOGRAPHY AND COGNITIVE IMPAIRMENT IN BUENOS AIRES, ARGENTINA
Bibliographic record
Abstract
Abstract Background The search for biomarkers for the early detection of mild cognitive impairment (MCI) and dementia continues, including ocular markers. An association between neurodegenerative diseases with thinning of optic nerve fiber layers (NFL), macular thickness (MT) and choroidal vessels has been shown by means of optical coherence tomography (OCT), since these structures correspond embryologically to the same origin. In this research we present our data from a population with mild cognitive impairment and dementia and the OCT findings. Method We carried out an observational, analytical, cross‐sectional study, comparing the OCT parameters (macular thickness and choroidal thickness) between people with cognitive impairment and a control group of patients treated in the Neurology and Ophthalmology services of the Italian Hospital from Buenos Aires between the years 2009 and 2019. The results were analyzed using the Kruskal‐Wallis and Wilcoxon test using the STATA v14 software Result We included 26 patients with MCI, 21 with dementia and a control group of 30 patients. Regarding the main objective, the measurement of macular thickness, we found statistically significant differences only in the quadrants according to ETDRS (Early Treatment Diabetic Retinopathy Study) greater than 3 mm and in the nasal one, 6 mm, both in the right eye (p < 0.02), finding very similar thickness values in the remaining quadrants. We were also unable to find differences in choroidal thickness between the groups under study (p0.27 in RE and p0.25 in LE) Conclusion In our study, we were unable to determine the association of cognitive impairment with significant thinning of the retinal macular thickness and choroidal thickness, either in early stages or in dementia, not knowing if this result derives from the way in which patients were selected or if there is another factor. It seems relevant to us to present these negative data and to propose future studies to clarify these results
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".