Regional comparisons of inpatient and outpatient patterns of cerebrovascular disease diagnosis in the province of Alberta.
Bibliographic record
Abstract
The diagnosis of cerebrovascular disease (CBVD) from administrative data has been critically examined by epidemiologists in recent years. Much of the existing literature suggests that hospital discharge diagnoses based on ICD-9-CM codes are an unreliable source of information for determining a diagnosis of stroke, particularly when four- and five-digit codes are used. We examined how diagnoses for CBVD in hospital inpatient and outpatient facilities vary between rural and urban areas and among the 16 administrative health regions. Our analysis revealed differences in diagnostic patterns between the two sources of data, differences between rural and urban areas, and variation across most of the regions. Geographic variation in health service utilization, diagnostic practices, specialty of the physician making the diagnosis, and disease burden may explain our findings. Our results suggest that the diagnosis of patients attending rural facilities are either coded differently (and less precisely) than those of urban residents or are coded more precisely only after the patients attend urban facilities. Regional differences in coding practices show that any CBVD surveillance system based on administrative data requires a large-scale (in this case, province-wide) and person-oriented approach.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".