Chemotherapy use in ovarian cancer patients diagnosed 2012–2017 in Australia, Canada, Norway and the UK: An International Cancer Benchmarking Partnership (ICBP) population-based study
Bibliographic record
Abstract
OBJECTIVE: To describe use of chemotherapy in patients with ovarian cancer in national or sub-national populations of Australia, Canada, Norway and the UK. METHODS: Linked population-based data sources were used to describe use and time to chemotherapy initiation in ovarian cancer patients diagnosed in study periods during 2012-2017. Random-effects meta-analysis characterised the size of interjurisdictional variation. RESULTS: Among 39,879 patients, chemotherapy use ranged from 49 % (Wales) to 75 % (Manitoba). Across jurisdictions, chemotherapy use was higher in advanced disease (79 %, 95 %CI: 74 %-83 %), and lower for stages 1-2 or localised/regional disease (54 %, 95 %CI: 48 %-60 %). Within jurisdictions, chemotherapy use was similar in patients aged 15-64 and 65-74 and then decreased sharply with increasing age. There was large interjurisdictional variation in chemotherapy use in patients aged 85-99 years with advanced disease, being, for example, 23 % (95 %CI: 20 %-25 %) in England and 61 % (95 %CI: 51 %-70 %) in Ontario. However, jurisdictions with the highest chemotherapy use in recorded advanced stage, including Ontario, tended to have higher percentage of missing stage information. Overall, time from diagnosis to chemotherapy initiation was shorter in New South Wales and Victoria and longer in Scotland and Wales. In patients with advanced disease, interjurisdictional variation in time-to-treatment was limited. CONCLUSIONS: Even within the same age groups and stage strata, use of chemotherapy varied substantially between jurisdictions during the mid-2010s. Future work should examine use of surgery in combination with chemotherapy. The reasons for the international variation in chemotherapy use and its contribution to international variation in survival should be established.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".