Determining the need and utilization of radiotherapy in cancers of the breast, cervix, lung, prostate, and rectum in Alberta, Canada.
Bibliographic record
Abstract
116 Background: Determining the appropriate rate of RT is important for health care planning and resource allocation. Establishing RT shortfalls (difference between observed and estimates of RT need) could provide an estimate of the capacity expansion that would be required to address them. Our primary objective was to determine the utilization of RT for cancers of the breast, cervix, lung, prostate and rectum in Alberta (AB), Canada. To determine the burden of RT shortfalls in AB, the secondary objective was to compare the observed AB RT rates to estimates of need derived from criterion-based benchmarking (CBB) and evidence-based estimates (EBEST). Methods: All incident cases of breast (B), cervix (C), lung (L), prostate (P) and rectal (R) cancers diagnosed in 2004-8 in AB were identified from the provincial cancer registry (ACR). Ethics board approval was obtained. Patients receiving RT within one year (RT-1y) of diagnosis were identified and grouped by cancer site. The proportion of cases receiving RT-1y was then calculated. Rates were compared using a Z statistic of the normal approximation for a difference in proportions. Estimates of the appropriate RT rate were derived from CBB and EBEST methods described in the literature. Results: A total of 68,164 cancer cases of interest were identified from the ACR. RT-1y rates for AB (95%CI) were: B: 50.5%(49.5-51.4), C: 45.7%(42.2-49.3), L: 36.5%(35.5-37.3), P:26.4%(25.6-27.3) and R:38.8%(37.1-40.6). Observed rates of RT in AB were lower than estimates derived using CBB and EBEST of RT-1y for B: 60.7%(59.3-62.1) and 57.1%(52.6-62.0), C: 48.6%(39.1-58.1) and 63.4%(61.1-65.7), L: 41.3%(39.9-42.7) and 44.6%(41.0-48.2), P: 37.2%(35.8-38.7) and 32.0%(28.4-36.0), and R: 43.4%(39.1-47.6) and 69.6%(68.7-70.5). Shortfalls varied across cancer sites according to whether CBB or EBEST estimates were referenced, ranging from 4.8% in lung cancer to 30.8% in rectal cancer. Conclusions: Important shortfalls exist in the utilization of RT in Alberta, Canada despite centralized cancer care and a publically funded health care system. The magnitude of the shortfall varied according to whether a CBB or EBEST estimate of RT was applied.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".