Bibliographic record
Abstract
Recently, we had occasion to anaesthetise a patient, who was susceptible to malignant hyperpyrexia, for the incision and drainage of a peri-anal abscess. She was a fit and healthy 19-year-old with no significant past medical history. She was well starved and we proposed to induce her with fentanyl 1–2 µg.kg−l, propofol 2 mg.kg−l and maintain anaesthesia with a propofol infusion. We had decided to let her breathe spontaneously on a laryngeal mask. However, on consideration, it was unclear whether the techniques used for cleaning laryngeal mask airways were sufficient to ensure that no volatile agent remained adsorbed onto the surface of a pre-used laryngeal mask. A discussion with Intavent followed and the company was also unsure as to whether normal cleaning would completely remove any traces of volatile agents from a laryngeal mask airway. A review of the literature shows that flushing silicone tubing with oxygen for over 1 h does not remove volatile agents and that normal decontamination procedures of rubber and silicone products reduce volatile anaesthetics to a variable extent [1]. Further review (Medline) has been unable to discover any cases of malignant hyperpyrexia triggered by the re-use of a laryngeal mask. We felt that to use a pre-used laryngeal mask would expose our patient to an unquantifiable, but unnecessary risk. Therefore, we used a disposable laryngeal mask airway in addition to the usual fresh breathing system and a vapour-free machine. The patient was anaesthetised without any ill effects.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".