Bibliographic record
Abstract
We appreciate the comments by Drs Grocott and Brudney.1 We agree that the specific language used in medical guidelines has the potential to influence the adoption rate by anesthesiologists. In this regard, we applaud the specific language by the American Society of Enhanced Recovery, the European Society of Anaesthesiology, and the Canadian Anesthesiologists Society regarding preoperative fasting guidelines that actively encourage patients to drink clear fluids up to 2 hours before elective surgery. We agree that guideline authors should attempt to be as explicit in their recommendations as the evidence and circumstances allow. But the final language chosen by a guideline committee must reflect a consensus opinion of the group. Thus, recommendations tend to be “watered down” and less explicit. The ambiguous nature of guidelines may also be intentional to hedge against its use during malpractice litigation. We applaud the work by Shiraishi et al2 because it provides evidence that it is safe to allow patients to drink until 2 hours before surgery. Evidence-based research enables authors of practice guidelines to make better and more specific recommendations. The outcome data regarding the benefit of carbohydrate beverages are limited. However, allowing patients to drink clear fluid preoperatively is humane and not associated with any harm. We challenge providers to implement process changes at their hospitals that will encourage their patients to drink clear fluids up to 2 hours before surgery. A decision support system within the electronic medical record can facilitate its implementation. Ramon E. Abola, MDTong J. Gan, MDDepartment of AnesthesiologyStony Brook MedicineStony Brook, New York[email protected]
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 | 0.004 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.349 | 0.209 |
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".