Correction: Patient Safety of Perioperative Medication Through the Lens of Digital Health and Artificial Intelligence
Bibliographic record
Abstract
Following the publication of "Patient Safety of Perioperative Medication Through the Lens of Digital Health and Artificial Intelligence" [1], concerns were raised regarding the corresponding author Jiancheng Ye's rate of self-citation within the article's reference list.Further investigation into this matter identified that numerous self-citations were added to the manuscript following acceptance of the article without adequate disclosure.The author was contacted for a response regarding this matter.The author indicated that the added references were pertinent to the article and its findings, and provided documentation justifying the inclusion of the identified self-citations.
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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 | 0.005 | 0.095 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.040 | 0.026 |
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".