In Raynaud phenomenon, on-demand sildenafil did not reduce disability or frequency or duration of attacks
Bibliographic record
Abstract
ACP Journal Club19 February 2019In Raynaud phenomenon, on-demand sildenafil did not reduce disability or frequency or duration of attacksReza Mirza, MD, Gordon Guyatt, MDReza Mirza, MDMcMaster University, Hamilton, Ontario, Canada (R.M., G.G.), Gordon Guyatt, MDMcMaster University, Hamilton, Ontario, Canada (R.M., G.G.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJ201902190-021 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Source CitationRoustit M, Giai J, Gaget O, et al. On-demand sildenafil as a treatment for Raynaud phenomenon: a series ofn-of-1 trials. Ann Intern Med. 2018;169:694-703. https://pubmed.ncbi.nlm.nih.gov/30383134Clinical Impact RatingsGIM/FP/GP: Rheumatology: References1 Herrick AL. Pathogenesis of Raynaud's phenomenon. Rheumatology (Oxford). 2005;44:587-96. [PMID: 15741200] Google Scholar2 Roustit M, Blaise S, Allanore Y, et al. Phosphodiesterase-5 inhibitors for the treatment of secondary Raynaud's phenomenon: systematic review and meta-analysis of randomised trials. Ann Rheum Dis. 2013;72:1696-9. [PMID: 23426043] Google Scholar Author, Article, and Disclosure InformationAffiliations: McMaster University, Hamilton, Ontario, Canada (R.M., G.G.)This article was published at Annals.org on 5 February 2019. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited bySildenafil beyond erectile dysfunction and pulmonary arterial hypertension: Thinking about new indications 19 February 2019Volume 170, Issue 4Page: JC21KeywordsBayesian methodDisabilitiesOutpatient clinicsOutpatientsPathogenesisPatientsPulmonary diseasesPulmonary hypertensionRheumatologyTemperature ePublished: 19 February 2019 Issue Published: 19 February 2019 Copyright & PermissionsCopyright © 2019 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 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".