Multiple Health Care Encounters Prior to Diagnosis of Cerebral Venous Thrombosis
Bibliographic record
Abstract
STUDY OBJECTIVE: Cerebral venous thrombosis symptom onset can be insidious and without focal deficits. We performed a planned analysis of care-seeking patterns prior to diagnosis in a Canadian randomized trial examining treatment and prognosis of cerebral venous thrombosis and its companion prospective observational registry to examine whether time to diagnosis or multiple health care encounters prior to diagnosis were associated with 180-day outcomes. METHODS: Adults within 14 days of diagnosis of a new symptomatic cerebral venous thrombosis were included. We examined timing from symptom onset to diagnosis and the number of health care encounters for cerebral venous thrombosis symptoms prior to diagnosis. We explored associations between multiple care encounters prior to diagnosis with patient demographics, baseline clinical and radiologic features, and 180-day outcomes. RESULTS: Of 102 patients (median age 45 [interquartile range {IQR} 31.0 to 61.0] years, 68.6% women), 40 (39%) had multiple health care encounters for their cerebral venous thrombosis symptoms prior to diagnosis. The median time from symptom onset to diagnosis was 4 (IQR 1 to 8) days. Women had a longer time from symptom onset to diagnosis compared with men (median 5 days [IQR 2 to 8 days] versus 2 days [IQR 1 to 4.5 days]), difference 3 days, 95% confidence interval [CI] 1 to 5 days) and were almost twice as likely as men to have had multiple health care encounters prior to diagnosis (46% versus 25%, difference 21%, 95% CI, 2 to 40). Time from symptom onset to diagnosis and number of health care encounters prior to diagnosis were not associated with adverse 180-day outcomes. CONCLUSION: Women were more likely to have multiple health care encounters prior to cerebral venous thrombosis diagnosis. However, this was not associated with worse outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".