Association Between Dementia and Optical Coherence Tomography Scan Quality
Bibliographic record
Abstract
It is generally assumed that dementia affects the quality of optical coherence tomography (OCT) scans. However, the magnitude of this effect and its independence from other factors require further clarification. In this cross-sectional study, our aim was to evaluate the association between cognitive impairment and OCT scan quality, adjusting for key confounders, in a multiethnic cohort. 541 participants aged 50 years or older were recruited from memory clinics and the community at the National University Hospital and St. Luke's Hospital, Singapore. They were then stratified into three groups: no cognitive impairment (NCI, n=112), cognitive impairment without dementia (CIND, n=235), and dementia (n=194); OCT scan quality was subsequently assessed based on the presence and severity of artifacts. We found that dementia patients were nearly three times more likely to produce poor-quality OCT scans compared to NCI participants (adjusted odds ratio [OR]=2.90; 95% CI, 1.24-6.80). Lower cognitive scores, including Mini-Mental State Examination (MMSE) (OR=0.92; 95% CI, 0.88-0.96), Montreal Cognitive Assessment (MoCA) (OR=0.90; 95% CI, 0.86-0.94), and higher Clinical Dementia Rating (CDR) scores (OR=2.11; 95% CI, 1.43-3.10), were also independently associated with poor scan quality. In conclusion, cognitive impairment, particularly dementia, substantially increases the likelihood of poor-quality OCT scans, even after accounting for key demographic and clinical factors. Hence, strategies tailored to improve imaging in this population are essential for enhancing diagnostic accuracy and patient care.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".