Evaluation of unmet clinical needs in prophylaxis and treatment of venous thromboembolism in at-risk patient groups: pregnancy, elderly and obese patients
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE) accounts for an estimated 900,000 cases per year in the US alone and constitutes a considerable burden on healthcare systems across the globe. OBJECTIVE: To understand why the burden is so high, qualitative and quantitative research was carried out to gain insights from experts, guidelines and published studies on the unmet clinical needs and therapeutic strategies in VTE prevention and treatment in three populations identified as being at increased risk of VTE and in whom VTE prevention and treatment were regarded as suboptimal: pregnant women, the elderly and obese patients. METHODOLOGY: A gap analysis methodology was created to highlight unmet needs in VTE management and to discover the patient populations considered most at risk. A questionnaire was devised to guide qualitative interviews with 44 thrombosis and haemostasis experts, and a review of the literature on VTE in the specific patient groups from 2015 to 2017 was completed. This was followed by a Think Tank meeting where the results from the research were discussed. RESULTS: This review highlights the insights gained and examines in detail the unmet needs with regard to VTE risk-assessment tools, biomarkers, patient stratification methods, and anticoagulant and dosing regimens in pregnant women, the elderly and obese patients. CONCLUSIONS: Specifically, in pregnant women at high risk of VTE, low-molecular-weight heparin (LMWH) is the therapy of choice, but it remains unclear how to use anticoagulants when VTE risk is intermediate. In elderly patients, evaluation of the benefit of VTE prophylaxis against the bleeding risk is particularly important, and a head-to-head comparison of efficacy and safety of LMWH versus direct oral anticoagulants is needed. Finally, in obese patients, lack of guidance on anticoagulant dose adjustment to body weight has emerged as a major obstacle in effective prophylaxis and treatment of VTE.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".