Correction to: Is the new ASNM intraoperative neuromonitoring supervision “guideline” a trustworthy guideline? A commentary
Bibliographic record
Abstract
The article Is the new ASNM intraoperative neuromonitoring supervision "guideline" a trustworthy guideline? A commentary, written by Stanley A. Skinner, Elif Ilgaz Aydinlar, Lawrence F. Borges, Bob S. Carter, Bradford L. Currier, Vedran Deletis, Charles Dong, John Paul Dormans, Gea Drost, Isabel Fernandez‑Conejero, E. Matthew Hoffman, Robert N. Holdefer, Paulo Andre Teixeira Kimaid, Antoun Koht, Karl F. Kothbauer, David B. MacDonald, John J. McAuliffe III, David E. Morledge, Susan H. Morris, Jonathan Norton, Klaus Novak, Kyung Seok Park, Joseph H. Perra, Julian Prell, David M. Rippe, Francesco Sala, Daniel M. Schwartz, Martín J. Segura, Kathleen Seidel, Christoph Seubert, Mirela V. Simon, Francisco Soto, Jeffrey A. Strommen, Andrea Szelenyi, Armando Tello, Sedat Ulkatan, Javier Urriza and Marshall Wilkinson, was originally published electronically on the publisher's internet portal (currently SpringerLink) on 05 January 2019 without open access. With the author(s)' decision to opt for Open Choice the copyright of the article changed on 30 January 2019 to © The Author(s) 2019 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The original article has been corrected.
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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 | 0.013 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.037 | 0.040 |
| Insufficient payload (model declined to judge) | 0.020 | 0.018 |
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".