Bibliographic record
Abstract
Corrections2 January 2018Correction: In the Clinic—Acute Kidney InjuryFREEThis correction concerns the following article:In the ClinicNov 2017Acute Kidney InjuryAndrew S. Levey, MD and Matthew T. James, MDAndrew S. Levey, MDFrom Tufts Medical Center, Boston, Massachusetts, and the University of Calgary, Calgary, Alberta. and Matthew T. James, MDFrom Tufts Medical Center, Boston, Massachusetts, and the University of Calgary, Calgary, Alberta.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/L17-0718 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail In a recent In the Clinic (1), "serum creatinine clearance" should be "serum creatinine concentration" in 3 places in Figure 1 and the footnote adjacent to the asterisk in Appendix Table 1.This has been corrected.Reference1. Levey AS, James MT. Acute kidney injury. Ann Intern Med. 2017;167:ITC66-80. [PMID: 29114754]. doi:10.7326/AITC201711070 LinkGoogle Scholar Comments0 CommentsSign In to Submit A Comment Author, Article, and Disclosure InformationAffiliations: PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoAcute Kidney Injury Andrew S. Levey and Matthew T. James Metrics Cited byPeroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α) overexpression alleviates endoplasmic reticulum stress after acute kidney injury 2 January 2018Volume 168, Issue 1Page: 84KeywordsAttentionCreatinineRenal failure ePublished: 2 January 2018 Issue Published: 2 January 2018 Copyright & PermissionsCopyright © 2018 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 | 0.004 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.166 | 0.105 |
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".