The Frequency of and Indications for General Anaesthesia in Children in Western Australia 2002–2003
Bibliographic record
Abstract
We conducted a retrospective database search of the Hospital Morbidity Data System at the Health Department of Western Australia to determine the number of anaesthetics given to children aged 16 years or less in Western Australia over a twelve-month period. Information was also collected to assess the types of surgery for which anaesthesia was being provided, and the categories of hospital in which children were being anaesthetized. We found that 28,522 anaesthetics were given to 24,981 children, and 2,462 (9.9%) children had more than one anaesthetic. Five and a half percent of the children in Western Australia had an anaesthetic during the twelve months studied. The most common types of surgery were ear nose and throat (28% of anaesthetics), general (21%), dental/oral procedures (17%) and orthopaedic (15%). There was a bimodal distribution in the incidence of anaesthesia versus age, with peaks at 4 years and at 16 years. The most common category of hospital that children were anaesthetized in was private metropolitan (40%) followed by tertiary (38%), rural (14%) and public metropolitan (8%). One thousand, seven hundred and seven children aged less than one year were given an anaesthetic. These anaesthetics were most frequently given to children in tertiary hospitals (62%) followed by private metropolitan (30%), public metropolitan (6%) and rural hospitals (2%).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".