<i>'Spot the CLOT':</i> Awareness of cancer-associated thrombosis in healthcare providers.
Bibliographic record
Abstract
This article is the third in a dedicated series entitled ‘Spot the CLOT’, which aims to raise awareness of cancer-associated thrombosis (CAT) to improve patient and provider education, patient outcomes and, ultimately, reduce the burden of CAT. The first two articles, entitled ‘The significance of VTE in cancer: Introduction of the Spot the CLOT’ (Sardo et al., 2021) and ‘Spot the CLOT: What cancer patients want to know’ (Bayadinova et al., 2022) described CAT, the knowledge deficits of CAT in cancer patients, and strategies for raising awareness and educating patients diagnosed with cancer. The focus of this third article in the series is promoting awareness of CAT in healthcare providers. It will identify knowledge gaps among medical personnel, suggest tools for identifying patients at highest risk, and offer strategies and available resources for increasing awareness among providers.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".