Real world duration of curative intent breast, colorectal, non-small cell lung, and prostate cancer treatment
Bibliographic record
Abstract
BACKGROUND: Advances in curative treatment for breast, colorectal, NSCLC and prostate cancer have led to improvements in cancer survival. Cancer treatment and recovery time can vary depending on the recommended modalities and intensity of therapy. Our objective was to determine the current real world duration of curative treatments for the four common cancers. METHODS: A retrospective review was completed of patients referred to BC Cancer from 2010 to 2016, ≤ 65 years old, newly diagnosed with stage I-III breast, colorectal, NSCLC or prostate cancer who received curative intent treatment. Information was collected on baseline characteristics, date of diagnosis, surgery, type, duration and intent of both radiotherapy and chemotherapy. RESULTS: In total, 22,275 patients were included: 55.7% breast, 22.4% colorectal, 9.2% NSCLC, 12.7% prostate cancer. Stage I/II/III at diagnosis: breast 47.2/38.7/14.1%, colorectal 26.5/30.1/43.5%, NSCLC 46.5/18.1/35.4%, prostate 7.7/62.9/29.4%. Patients treated with definitive surgery only: breast 35.9%, colorectal 58%, NSCLC 52.2%, prostate 40.1%. The median duration of multimodality treatment was breast 24.6 weeks, colorectal 26.7 weeks, NSCLC 9.1 weeks, and prostate 6.0 weeks. CONCLUSIONS: Approximately half of patients who undergo curative cancer treatment require definitive radiotherapy or multimodality treatment. The median duration of therapy for the most commonly treated cancers ranged from 6.0-26.7 weeks. Multimodality curative treatment can be prolonged for selected cancers when accounting for the duration of adjuvant chemotherapy and radiotherapy and recovery time between modalities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".