Predictors of diagnostic delays and loss to follow-up in women with von Willebrand disease: a single-center retrospective cohort study
Bibliographic record
Abstract
Background: Women with von Willebrand disease (VWD) often face diagnostic delays, leading to increased bleeds, stress, and healthcare use. The factors influencing these delays and their effects on gynecologic outcomes are not well understood. Objectives: This study aimed to 1) identify the prevalence and predictors of diagnostic delays and loss to follow-up in women with VWD and 2) determine how these delays affect severe gynecologic bleeding, emergency visits, transfusions, and hysterectomies. Methods: We conducted a single-center retrospective cohort study and included women aged ≥18 years diagnosed with VWD. Delayed diagnosis was defined as ≥3 bleeding events prior to VWD diagnosis, excluding easy bruising due to its subjectivity. Loss to follow-up was defined as ≥5 years since the last hematology visit. We used logistic regression for analysis. Results: Among 178 diagnosed women (median age, 27 years), 71 (40%) experienced ≥3 bleeding events before diagnosis. The median time from the first bleeding event to VWD diagnosis was 14.2 years. Severe bleeding events significantly predicted diagnostic delays (adjusted odds ratio, 3.1; 95% CI, 1.5-6.2). Fifty-four (30%) women were lost to follow-up, with remote era of initial bleed and VWD type identified as significant predictors. Delays were associated with increased risks of hysterectomies (odds ratio, 2.7; 95% CI, 1.2-6.3) and other gynecologic procedures. Conclusion: Delayed diagnosis and loss to follow-up in VWD are common even in a specialized Hemophilia Treatment Centre. Such delays lead to more severe bleeding and increased gynecologic interventions. Prompt diagnosis is paramount for better patient outcomes and reduced healthcare utilization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".