Comparative Effectiveness of Routine Invasive Coronary Angiography for Managing Unstable Angina
Bibliographic record
Abstract
Letters5 December 2017Comparative Effectiveness of Routine Invasive Coronary Angiography for Managing Unstable AnginaVijaya Sundararajan, MD, MPH and Sara Vogrin, MBBS, MBiostatVijaya Sundararajan, MD, MPHSt. Vincent's Hospital, University of Melbourne, Fitzroy, Victoria, Australia (V.S., S.V.)Search for more papers by this author and Sara Vogrin, MBBS, MBiostatSt. Vincent's Hospital, University of Melbourne, Fitzroy, Victoria, Australia (V.S., S.V.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L17-0522 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We thank Dr. Oshima and colleagues for their query about whether we adequately accounted for unmeasured confounders in our analysis. As described in the Statistical Analysis, Sensitivity Analysis, and Discussion sections and in Figure 4 of our article, we tested whether 1 or more unmeasured confounders might negate the observed effect size of ICA. We used the example of whether greater provision of optimal medical therapy (OMT) to the group that received ICA versus the comparison group could account for part or all of the observed effect size in our original analysis and will expand on that example.... Reference1. Yan AT, Yan RT, Tan M, Huynh T, Soghrati K, Brunner LJ, et al; Canadian ACS Registries Investigators.. Optimal medical therapy at discharge in patients with acute coronary syndromes: temporal changes, characteristics, and 1-year outcome. Am Heart J. 2007;154:1108-15. [PMID: 18035083] CrossrefMedlineGoogle Scholar2. Chernomordik F, Sabbag A, Tzur B, Kopel E, Goldkorn R, Matetzky S, et al. Cardiac rehabilitation following an acute coronary syndrome: trends in referral, predictors and mortality outcome in a multicenter national registry between years 2006-2013: report from the Working Group on Cardiac Rehabilitation, the Israeli Heart Society. Eur J Prev Cardiol. 2017;24:123-32. [PMID: 27881758] doi:10.1177/2047487316680692 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: St. Vincent's Hospital, University of Melbourne, Fitzroy, Victoria, Australia (V.S., S.V.)Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M16-2420. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoComparative Effectiveness of Routine Invasive Coronary Angiography for Managing Unstable Angina Sara Vogrin , Richard Harper , Elizabeth Paratz , Andrew MacIsaac , Jodie Burchell , Belinda Smith , Anthony Scott , Jongsay Yong , and Vijaya Sundararajan Comparative Effectiveness of Routine Invasive Coronary Angiography for Managing Unstable Angina Yasuo Oshima , Yuko Morishima , Koichi Ono , Masaya Hirano , and Katsunori Suzuki Metrics 5 December 2017Volume 167, Issue 11Page: 836KeywordsAcute coronary syndromeAngiographyCardiac rehabilitationConflicts of interestDisclosureHazard ratioOdds ratioRelative riskUnstable angina ePublished: 5 December 2017 Issue Published: 5 December 2017 Copyright & PermissionsCopyright © 2017 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 | 0.021 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".