Clinical Challenges in Patients with Cancer-Associated Thrombosis: Canadian Expert Consensus Recommendations
Bibliographic record
Abstract
Venous thromboembolism is a common complication in cancer patients, and thromboembolism is the second most common cause of death after cancer progression. A number of clinical practice guidelines provide recommendations for the management of cancer-associated thrombosis. However, the guidelines lack recommendations covering commonly encountered clinical challenges (for example, thrombocytopenia, recurrent venous thromboembolism, etc.) for which little or no evidence exists. Accordingly, recommendations were developed to provide expert guidance to medical oncologists and other health care professionals caring for patients with cancer-associated thrombosis. The current expert consensus was developed by a team of 21 clinical experts. For each identified clinical challenge, the literature in medline, embase, and Evidence Based Medicine Reviews was systematically reviewed. The quality of the evidence was assessed, summarized, and graded. Consensus statements were generated, and the experts voted anonymously using a modified Delphi process on their level of agreement with the various statements. Statements were progressively revised through separate voting iterations and were then finalized. Clinicians using these recommendations and suggestions should tailor patient management according to the risks and benefits of the treatment options, patient values and preferences, and local cost and resource allocations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.088 | 0.182 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".