sj-docx-1-cjk-10.1177_20543581211069225 – Supplemental material for Effect of a Perioperative Hypotension-Avoidance Strategy Versus a Hypertension-Avoidance Strategy on the Risk of Acute Kidney Injury: A Clinical Research Protocol for a Substudy of the POISE-3 Randomized Clinical Trial
Bibliographic record
Abstract
Supplemental material, sj-docx-1-cjk-10.1177_20543581211069225 for Effect of a Perioperative Hypotension-Avoidance Strategy Versus a Hypertension-Avoidance Strategy on the Risk of Acute Kidney Injury: A Clinical Research Protocol for a Substudy of the POISE-3 Randomized Clinical Trial by Amit X. Garg, Meaghan Cuerden, Hector Aguado, Mohammed Amir, Emilie P. Belley-Cote, Keyur Bhatt, Bruce M. Biccard, Flavia K. Borges, Matthew Chan, David Conen, Emmanuelle Duceppe, Sergey Efremov, John Eikelboom, Edith Fleischmann, Landoni Giovanni, Peter Gross, Raja Jayaram, Mikhail Kirov, Ydo Kleinlugtenbelt, Andrea Kurz, Andre Lamy, Kate Leslie, Valery Likhvantsev, Vladimir Lomivorotov, Maura Marcucci, Maria José Martínez-Zapata, Michael McGillion, William McIntyre, Christian Meyhoff, Sandra Ofori, Thomas Painter, Pilar Paniagua, Chirag Parikh, Joel Parlow, Ameen Patel, Carisi Polanczyk, Toby Richards, Pavel Roshanov, Denis Schmartz, Daniel Sessler, Tim Short, Jessica M. Sontrop, Jessica Spence, Sadeesh Srinathan, David Stillo, Wojciech Szczeklik, Vikas Tandon, David Torres, Thomas Van Helder, Jessica Vincent, C. Y. Wang, Michael Wang, Richard Whitlock, Maria Wittmann, Denis Xavier and P. J. Devereaux in Canadian Journal of Kidney Health and Disease
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.895 | 0.307 |
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".