Hysterectomy trends in Australia – between 2000/01 and 2004/05
Bibliographic record
Abstract
BACKGROUND: Hysterectomy is a major and common surgical procedure that has the potential to provide relief from ongoing gynaecological problems, but is often associated with negative impacts on health and wellbeing. Research indicates that hysterectomy rates and trends vary widely between and within countries; yet little is known about patterns in Australia. AIMS: This research aimed to describe hysterectomy rates and trends in Australia between 2000/01 and 2004/05. METHODS: This repeat cross-sectional study used routinely collected data from all hospitals in Australia. Data on all women admitted to hospital for a hysterectomy were obtained from the National Hospital Morbidity Database (2000/01-2004/05). Data were analysed by calculating population rates for each type of hysterectomy. Incidence rate ratios were calculated to assess changes over time. RESULTS: Hysterectomy rates in Australia declined from 34.8 per 10 000 women in 2000/01 to 31.2 per 10 000 women in 2004/05. A decline in the incidence rate for abdominal hysterectomy (from 18.7 to 15.1 per 10 000 women) and the incidence rate for concurrent oophorectomy (from 12.4 to 11.3 per 10 000 women) were also observed during this time period. At each point in time, the highest incidence rates for hysterectomy were for women aged 45-54 years. CONCLUSIONS: Hysterectomy rates in Australia are declining over time and currently appear to be lower than most other countries. More hysterectomies are performed vaginally than in Canada, the USA, the UK and Finland and the rate of concurrent oophorectomy is less than that reported in the USA and the UK.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".