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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".