Bibliographic record
Abstract
UpToDate| May 2023 UpToDate® ASA Monitor May 2023, Vol. 87, 11. https://doi.org/10.1097/01.ASM.0000935260.74321.2d Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Cite Icon Cite Get Permissions Search Site Citation UpToDate®. ASA Monitor 2023; 87:11 doi: https://doi.org/10.1097/01.ASM.0000935260.74321.2d Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll PublicationsASA Monitor Search Advanced Search Topics: cannabinoids, cannabis, covid-19 testing, guidelines, marijuana, perioperative care, preoperative care UpToDate® and ASA Monitor are collaborating to present select content abstracts on “What's New in Anesthesiology.” UpToDate is an evidence-based, clinical support resource used worldwide by healthcare practitioners to make decisions at the point of care. For complete, current “What's New” content, or to become a subscriber for full content access, go to www.uptodate.com. “What's New” abstract information is free for all medical professionals. Avoiding unnecessary resource use and controlling emissions are important approaches to reduce the environmental impact of perioperative care. Updated Canadian Anesthesiologists' Society guidelines re-emphasize specific strategies that include choosing reusable, reprocessable equipment rather than single-use disposable items, recycling materials when feasible, and responsibly using inhalation anesthetic agents (e.g., low fresh gas flow during delivery; minimizing use of desflurane and nitrous oxide; selecting alternative techniques such as total intravenous anesthesia [TIVA] or neuraxial or regional anesthetic approaches when appropriate).1 In December 2022, the... You do not currently have access to this content.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.887 | 0.888 |
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".