Abstract P243: Identifying Cerebral Venous Thrombosis Through Administrative Data: Icd-10 Case Ascertainment Depends on Clinical Context
Bibliographic record
Abstract
Background: Recent reported population-based rates of cerebral venous thrombosis (CVT) are higher than in older studies, though the context of these diagnoses is not well-defined. To better understand these trends, we examined the accuracy of administrative codes ( ICD-10 ) for CVT in different clinical scenarios. Methods: Cases of CVT presenting to a tertiary center between 2008-2018 were identified in two ways: free text search through all hospital electronic radiology reports regardless of modality and body part and any ICD-10 discharge codes (see Table 1). Electronic medical records were reviewed to verify diagnoses of CVT and their clinical context (Figure 1) to calculated Positive Predictive Value (PPV) of ICD-10 codes. Additionally, sensitivities of ICD-10 codes were calculated against all CVTs identified using either searches that were verified on chart review as the gold standard. Results: There were 289 confirmed cases: 239 new diagnoses, 204 of which were acute events. Only 75 cases (37%) were new, symptomatic CVTs not provoked by trauma or structural processes. Sensitivity and PPV for ICD-10 codes depending on clinical context is reported in Table 1. Conclusion: The majority of CVT identified at our institution were incidentally diagnosed in context of intracranial processes such as trauma, surgery, infection, or masses; 37% were symptomatic, non-structural incident diagnoses. Our findings have implications in interpreting CVT rates identified through administrative data, as the management and prognosis of CVT may differ based on clinical context.
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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 | 0.009 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".