Assessing the Hereditary Hemorrhagic Telangiectasia Algorithms in a Community-Based Patient Population
Bibliographic record
Abstract
INTRODUCTION: Hereditary hemorrhagic telangiectasia (HHT) is a rare, genetic, and underdiagnosed disease that causes vascular malformations throughout the body. Two specific combinations of International Classification of Diseases, Ninth Revision-Clinical Modification diagnosis codes, the "HHT Algorithms" (HHTAs), were developed previously from a derivation cohort to help identify undiagnosed HHT cases. OBJECTIVES: To test these 2 algorithms, and a third, newly designed HHTA, in an independent population with available clinical records and thus identify people who might have undiagnosed HHT. METHODS: The HHTAs were applied to the patient population of Kaiser Permanente Northern California. The HHTAs produced 3 groups (A, B, and C) using different combinations of diagnosis codes reflecting clinical manifestations of HHT. First, the number of Kaiser Permanente Northern California patients with each code was determined by database programming. Next, detailed chart review was performed, and patients with a Curaçao score of 2 or higher were considered to have possible HHT. RESULTS: Of 3,065,210 records queried, 163 patients met HHTA criteria. After chart review, the study identified 113 patients with possible undiagnosed HHT (Group A: n = 3, Group B: n = 3, Group C: n = 107). CONCLUSION: Employing the HHTAs in this community-based population resulted in a modest yield of patients with possible HHT. Further research is required to assess the utility of the HHTAs in identifying patients with actual HHT.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".