Review: Horse chestnut seed extract is effective for symptoms of chronic venous insufficiency
Bibliographic record
Abstract
TherapeuticsJuly 1, 2006Review: Horse chestnut seed extract is effective for symptoms of chronic venous insufficiencySusan R. Kahn, MD, MScSusan R. Kahn, MD, MScSir Mortimer B. Davis Jewish General Hospital, Montreal, Quebec, Canada (S.R.K.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJC-2006-145-1-020 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinkedInRedditEmail Source CitationPittler MH, Ernst E. Horse chestnut seed extract for chronic venous insufficiency. Cochrane Database Syst Rev. 2006;(1):CD003230. https://pubmed.ncbi.nlm.nih.gov/16437450Clinical Impact RatingsGIM/FP/GP: Hematology: References1 Heit JA, Rooke TW, Silverstein MD, et al. Trends in the incidence of venous stasis syndrome and venous ulcer: a 25-year population-based study. J Vasc Surg. 2001;33:1022-7. [PMID: 11331844] Google Scholar2 Kurz X, Kahn SR, Abenhaim L, et al. Chronic venous disorders of the leg: epidemiology, outcomes, diagnosis and management. Summary of an evidence-based report of the VEINES task force. Venous Insufficiency Epidemiologic and Economic Studies. Int Angiol. 1999;18:83-102. [PMID: 10424364] Google Scholar Author, Article, and Disclosure InformationAffiliations: Sir Mortimer B. Davis Jewish General Hospital, Montreal, Quebec, Canada (S.R.K.) PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails July 1, 2006Volume 145, Issue 1Page: 20KeywordsAdverse eventsAnklesClinical trialsDrugsEdemaHeadachesHypertensionInflammationInformation storage and retrievalMedical conditionsNauseaPeripheral vascular diseasePrevention, policy, and public healthPruritusSafetySurgeryVascular medicine ePublished: 9 March 2020 Issue Published: July 1, 2006 Copyright & PermissionsCopyright © 2006 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".