A Lesson Learned
Bibliographic record
Abstract
Thank you for giving us this opportunity to respond. We wish to apologize to the editorial staff and readership of Anesthesiology and the Canadian Journal of Anesthesia .In October 2004, we published a study in the Canadian Journal of Anesthesia ,1and in January 2005, we published another article in Anesthesiology.2At the time of the Anesthesiology submission, we failed to notify the journal of the existence of the previous article, which used the same data set and had similar methodology. Although the article in Anesthesiology expanded on the findings reported in the Canadian Journal of Anesthesia , this is inconsistent with our acknowledgment of the Instructions for Authors, which states, “Submitted manuscripts must not have been published elsewhere, in whole or in part.” We realize that journals must take this issue seriously. It is necessary to maintain integrity and provide the peer review process the complete information to thoroughly evaluate a manuscript and arrive at the optimal decision. Although not intended to hide the existence of another manuscript, our actions did not allow this process to occur. We take the issue of academic integrity seriously, and we are sincerely sorry for causing this situation.* Duke University Medical Center, Durham, North Carolina. niels006@mc.duke.edu
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.076 | 0.037 |
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".