Chemotherapy and radiotherapy use in patients with lung cancer in Australia, Canada, the UK and Norway 2012–2017: an ICBP population-based study
Bibliographic record
Abstract
Background: International variation in lung cancer survival may be partly explained by variation in stage-specific treatment use, but relevant comparative evidence is sparse. As part of the International Cancer Benchmarking Partnership, we examined use of chemotherapy and radiotherapy in population-based cancer registry data. Methods: Linked population-based data sources were used to describe use and time to first treatment for either chemotherapy or radiotherapy in patients with lung cancer diagnosed in study periods during 2012-2017 in 16 jurisdictions of Australia, Canada, the UK and Norway. Results: There was large variation in the proportions of patients with lung cancer receiving chemotherapy (ranging from 23% in Northern Ireland to 45% in Norway) and radiotherapy (ranging from 32% in England to 48% in New South Wales and 50% in Newfoundland and Labrador). Across jurisdictions, chemotherapy use decreased steeply with increasing age, regardless of stage at diagnosis. For radiotherapy use, in stage 1-3 cancer three patterns were observed: (a) steep decrease with increasing age (UK jurisdictions, Saskatchewan-Manitoba); (b) a relatively flat pattern (Norway, Alberta, British Columbia, Atlantic Canada, New South Wales) and (c) increasing use with increasing age (Ontario).Time to radiotherapy initiation was longer in the UK jurisdictions than elsewhere; time to chemotherapy was longer in the UK and Canadian jurisdictions except Ontario. Discussion: Use of chemotherapy and radiotherapy in patients with lung cancer varied substantially between jurisdictions during the mid-2010s within age-stage strata. Reasons for these variations are unclear. Differences in non-surgical treatment use are plausibly associated with international variation in lung cancer survival.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".