Utilization of a surgical database to provide care and assess perioperative treatment and outcomes in patients with bleeding disorders
Bibliographic record
Abstract
OBJECTIVES: To describe the Indiana Hemophilia and Thrombosis Center (IHTC) surgical database, its key components, and exploratory analyses of surgeries conducted between 1998 and 2019. METHODS: Surgical data across bleeding disorders collected retrospectively (1998-2006) and prospectively (2006-2019) were analyzed. Perioperative hemostasis, complications, and surgical plan deviations were compared by bleeding disorder diagnosis and data collection period. RESULTS: Within the 21-year period, 3246 procedures were conducted in 1413 patients with a diagnosis of von Willebrand disease (vWD), hemophilia A (HA), hemophilia B (HB), and other bleeding disorders. Majority of the procedures were minor (63.3%), and median number of surgeries per patient was 1 (range: 1-22). Adequate perioperative hemostasis was achieved in 90.9%, complications occurred in 13.6%, and surgical plan deviations occurred in 31.3% of procedures. Inadequate perioperative hemostasis and surgical plan deviations occurred more frequently in procedures involving HB compared with other bleeding disorders. Complications were not significantly different across bleeding disorders (p = .164). The prospective data collection period was associated with higher rates of hemostatic efficacy (92.4% vs. 88.3%; p < .001), complications (14.3% vs. 12.3%; p < .001), and plan deviations (34.2% vs. 25.1%; p < .001). CONCLUSION: The surgical database is an important resource in surgical management in patients with bleeding disorders. Further evaluation will facilitate use for the development of predictive models and principles of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".