Evaluation of unmet clinical needs in prophylaxis and treatment of venous thromboembolism in high-risk patient groups: cancer and critically ill
Bibliographic record
Abstract
BACKGROUND: Clinical practice shows that venous thromboembolism (VTE) presents a substantial burden in medical patients, and awareness and advocacy for its primary and secondary prevention remains inadequate. Specific patient populations, such as those with cancer and the critically ill, show elevated risk for VTE, bleeding or both, and significant gaps in VTE prophylaxis and treatment exist in these groups. OBJECTIVE: To present novel insights and consolidated evidence collected from experts, clinical practice guidelines and original studies on the unmet needs in thromboprophylaxis, and on the treatment of VTE in two high-risk patient groups: patients with cancer and the critically ill. METHODOLOGY: To identify specific unmet needs in the management of VTE, a methodology was designed and implemented that assessed gaps in prophylaxis and treatment of VTE through interviews with 44 experts in the field of thrombosis and haemostasis, and through a review of current guidelines and seminal studies to substantiate the insights provided by the experts. The research findings were then analysed, discussed and consolidated by a multidisciplinary group of experts. RESULTS: The gap analysis methodology identified shortcomings in the VTE risk assessment tools, patient stratification approaches for prophylaxis, and the suboptimal use of anticoagulants for primary prophylaxis and treatment. CONCLUSIONS: Specifically, patients with cancer need better VTE risk assessment tools to tailor primary thromboprophylaxis to tumour types and disease stages, and the potential for drug-drug interactions needs to be considered. In critically ill patients, unfractionated heparin is not advised as a first-line treatment option, and the strength of evidence is increasing for direct oral anticoagulants as a treatment option over low-molecular-weight heparins.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| 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 | 0.000 | 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".