Arguments against Monitoring Levels of Anti-Factor Xa in Conjunction with Low-Molecular-Weight Heparin Therapy
Bibliographic record
Abstract
ABSTRACT The product monographs for some low-molecular-weight heparins (LMWHs) state that anti-factor Xa concentrations should be monitored in the treatment of deep vein thrombosis. This article reviews the pharmacological and epidemiological basis for this recommendation and finds the evidence wanting. Anti-factor Xa activity varies for each LMWH, and evidence from clinical trials using anti-factor Xa monitoring does not support a link between activity of anti-factor Xa, bleeding, and effect of the drug. Given this lack of evidence, routine monitoring of anti-factor Xa in conjunction with LMWH therapy is not recommended. RESUME Les monographies de produit de certaines heparines de faible poids moleculaire (HFPM) indiquent que les concentrations de l’anti-facteur Xa doivent etre surveillees dans le traitement de la thrombose veineuse profonde. Cet article analyse les fondements pharmacologiques et epidemiologiques de cette recommandation et trouve les donnees insuffisantes. L’activite de l’antifacteur Xa varie pour chaque HFPM et les donnees tirees des essais cliniques qui ont eu recours a la mesure de l’anti-facteur Xa n’ont pu etablir aucun lien entre l’activite de l’anti-facteur Xa, le saignement et l’effet du medicament. Compte tenu des donnees insuffisantes, il n’est pas recommande d’effectuer la surveillance systematique de l’anti-facteur Xa dans le cadre d’un traitement a l’heparine de faible poids moleculaire.
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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 | 0.104 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".