Systemic, local, and sclerotherapy drugs: What do we know about drug prescribing in vascular anomalies?
Bibliographic record
Abstract
Off-label drug prescribing, frequent in the treatment of vascular anomalies (VA), relies on the quality of the literature reporting drug efficacy and safety. Our objective is to review the level of evidence (LOE) surrounding drug use in VA, which is more prevalent in pediatric care. A list of drugs used in VA was created with a literature review in July 2020. For each drug listed, the article displaying the highest LOE was determined and then compared between efficacy/safety data, routes of administration, pharmacological categories and a subset of VA. The influence of research quality on study results was also explored. The median LOE for the 74 drugs identified poor methodological quality, with a predominance of retrospective studies or case reports. Drug safety is currently inadequately reported. This is alarming as many treatments display significant safety concerns. Also, current literature displays major publication bias that probably leads to overestimation of drug efficacy in VA.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".