Bibliographic record
Abstract
Although I wholeheartedly agree with Dr I. G. Johnston (Anaesthesia 1999; 54: 1122) about the need to enforce our position as physician anaesthetists, there are a few comments I wish to make His implication that the care provided to critically ill medical ITU patients by anaesthetic trainees may not be optimal does grave injustice to the many dedicated consultant anaesthetists and excellent nurses who are involved in managing these challenging patients. Dr Johnston has to remember that trainees should not, and must not be expected to, look after these patients on their own but should be supported appropriately by nurses and consultants working as a team. This system has always worked well to provide these patients with the quality care that they require. Like Dr Johnston, we all have our share of patients who present for surgery but yet have untreated/nonoptimised pre-operative medical problems. Many of us would attempt to optimise these patients to the best of our ability, which sometimes may not be good enough. This is the time when the expertise of the medical consultant should be sought. As the duty of care to a patient requiring an anaesthetic ultimately falls upon the anaesthetist, it is probably inappropriate to seek a medical consultant's pronouncement about a patient's fitness for anaesthesia. This is, hopefully, a dying practice. Finally, spending 6–12 months training in Medicine is certainly a positive step towards the making of a better-equipped anaesthetist but we are well aware that there are many anaesthetists from pre-Calman days who never had such an opportunity yet excel in their roles. Then again, would 6–12 months be adequate? Rather, I would maintain that it is the process of continuing medical education, self-revalidation and partaking in learning opportunities, such as interdisciplinary meetings, that add to one's skills. Most importantly, the apprenticeship role of the Calman trainee still plays an important part in the training of an anaesthetist.
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 | 0.034 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.024 |
| 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".