Transient Ischemic Attack (TIA) Incidence with Geographic Information Systems (GIS) Mapping for Stroke Prevention Interventions
Bibliographic record
Abstract
OBJECTIVES: GIS mapping as a public health tool has been increasingly applied to chronic disease control. While evaluating TIA incidence from an existing regional stroke registry in Ludhiana city, India, we aim to apply the innovative concept of regional TIA GIS mapping for planning targeted stroke prevention interventions. METHODS: TIA patient data was obtained from hospitals, scan centers and general practitioners from March 2010 to March 2013 using WHO-Stroke STEPS based surveillance as part of establishing a population-based stroke registry in Ludhiana city. From this registry, patients with TIA (diagnosed by MRI image-based stroke rule-out, or clinically) were chosen and data analyzed. RESULTS: A total of 138 TIA patients were included in the final analysis. The annual TIA incidence rate for Ludhiana city was 7.13/100,000 (95% confidence interval: 5.52 to 8.74) for 2012-2013. Mean age was 58.5 ± 13.9 years (range: 22-88 years) and 87 (63%) were men. Majority of the TIA cases had anterior circulation TIAs. Hypertension (87.4%) was the most common risk factor. Using Geographic Information System (GIS) mapping, high TIA incidence was seen in central, western, and southern parts and clustering of TIA cumulative incidence was seen in the central part of Ludhiana city. CONCLUSION: Incidence rate of TIA was lower than that expected from a low- and middle-income country (LMIC). TIA GIS mapping, looking at regional localization, can be a novel option for developing targeted, cost-effective stroke prevention programs.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".