Bibliographic record
Abstract
After reading recent correspondence regarding a failure of an Ohmeda Tec 5 generation vaporiser (Fernando & Peck. Anaesthesia 2001; 56 :1009–10), I was left with the impression that they described a relatively freak event which was unlikely to be repeated. However, a recent event in our hospital revealed it was relatively easy to overfill this generation of vaporisers without any tilting at all. In fact, several of our vaporisers were overfilled on the same evening. This overfilling may be achieved simply by loosening the filler from the bottle and filling with the vaporiser turned on, manoeuvres that have been used to speed the filling process. Despite our staff being familiar with the current recommendations of the AAGBI [] regarding checking anaesthetic equipment, the overfilling was only noticed when an oversupply of vapour (6.5% when dialled to 1.5%) triggered an alarm on the anaesthetic agent monitor. Fortunately, this incident had no adverse effect on our patient. However, if the faulty vaporiser had been in one of our anaesthetic induction rooms without volatile agent monitoring, and the patient had been less fit, this may not have been the case. This event highlighted the fact that no‐one in our department was aware that these later generation vaporisers could be overfilled, with some convinced by their previous experience that it was impossible. We believe it likely that a similar belief is common around the country. This lack of awareness had contributed to a less diligent checking of the vaporisers and reduced emphasis on training those who filled them. A similar problem with overfilling has been described with older models and led to a Safety Action Bulletin from the Department of Health in 1992 []. There were also interesting discussions in the Canadian press around the same time []. Our incident has taught us an old lesson.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".