Commentary on: Nanotechnology and Artificial Intelligence: An Emerging Paradigm for Postoperative Patient Care
Bibliographic record
Abstract
Journal Article Accepted manuscript Commentary on: Nanotechnology and Artificial Intelligence: An Emerging Paradigm for Postoperative Patient Care Get access Tyler Safran, MD, MSc, Tyler Safran, MD, MSc From the Division of Plastic & Reconstructive Surgery, McGill University, Montreal, Canada https://orcid.org/0000-0002-0795-3109 Search for other works by this author on: Oxford Academic PubMed Google Scholar Joshua Vorstenbosch, MD, PHD, FRCSC, Joshua Vorstenbosch, MD, PHD, FRCSC From the Division of Plastic & Reconstructive Surgery, McGill University, Montreal, Canada Search for other works by this author on: Oxford Academic PubMed Google Scholar Mirko Gilardino, MD, MSc, FRCSC, FACS Mirko Gilardino, MD, MSc, FRCSC, FACS From the Division of Plastic & Reconstructive Surgery, McGill University, Montreal, Canada Corresponding author: Dr Mirko S. Gilardino, 1650 Cedar Avenue Room D6.269, Montreal QC, H3G 1A4 E-mail; mirko.gilardino@muhc.mcgill.ca; Instagram: @McGillPRS Search for other works by this author on: Oxford Academic PubMed Google Scholar Aesthetic Surgery Journal, sjad078, https://doi.org/10.1093/asj/sjad078 Published: 29 March 2023 Article history Received: 10 March 2023 Revision received: 12 March 2023 Accepted: 13 March 2023 Published: 29 March 2023
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".