Cardiovascular risk following transient vision loss
Bibliographic record
Abstract
BACKGROUND/AIMS: To evaluate short- and long-term cardiovascular risk following a first diagnosis of transient vision loss (TVL) compared with matched controls using the TriNetX research network. METHODS: Patients with an incident diagnosis of TVL were retrospectively identified and 1:1 propensity score matched to controls with dry eye syndrome. Primary outcomes included major adverse cardiovascular events (MACE), stroke, myocardial infarction (MI), ventricular arrhythmias, venous thromboembolism (VTE), hospitalisation and all-cause mortality. Cox proportional hazards models estimated hazard ratios (HRs) from 14 days to 10 years. Subgroup analyses evaluated patients free of events at 90 days and 1 year. RESULTS: After matching, 37 750 patients were included in each cohort. Mean (SD) age was 56.8 (16.8) years in the TVL cohort (59.7% female) and 56.6 (16.3) years in the control cohort (58.9% female). Within 14 days, stroke risk increased over 21-fold (HR 21.7; 95% CI 13.4 to 37.4), MACE nearly 10-fold (HR 9.80; 95% CI 7.19 to 13.34), arrhythmia over fourfold (HR 4.01; 95% CI 2.72 to 5.90), MI fivefold (HR 5.00; 95% CI 1.92 to 12.06) and hospitalisation nearly fourfold (HR 3.83; 95% CI, 3.52 to 4.17) compared with controls. VTE risk was modest and transient, with no elevation beyond 5 years and all-cause mortality was not elevated at any time point. Among patients' event-free at 90 days or 1 year, elevated long-term risk persisted up to 10 years for MACE, stroke, arrhythmiaand hospitalisation. CONCLUSIONS: TVL is associated with increased short- and long-term risks of MACE, stroke, MI, arrhythmia and hospitalisation, warranting prompt systemic evaluation and long-term monitoring.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".