The Cost of Failed First-Line Cancer Treatment Related to Continued Smoking in Canada
Bibliographic record
Abstract
Background: Smoking by cancer patients and survivors causes adverse cancer treatment outcomes, but little information is available about how smoking can affect cancer treatment costs. Methods: We developed a model to estimate attributable cancer treatment failure because of continued smoking after a cancer diagnosis (AFs). Canadian health system data were used to determine the additional treatment cost for AFs for the most common cancers in Canada. Results: Of 206,000 patients diagnosed with cancer annually, an estimated 4789 experienced afs. The annual incremental cost associated with treating patients experiencing afs was estimated at between $198 million and $295 million (2017 Canadian dollars), reflecting an added incremental cost of $4,810–$7,162 per patient who continued to smoke. Analyses according to disease site demonstrated higher incremental costs where the smoking prevalence and the cost of individual second-line cancer treatment were highest. Of breast, prostate, colorectal, and lung cancers, lung cancer was associated with the highest incremental cost for treatment after AFs. Conclusions: The costs associated with afs in Canada after a cancer diagnosis are considerable. Populations in which the smoking prevalence and treatment costs are high are expected to benefit the most from efforts aimed at increasing smoking cessation capacity for patients newly diagnosed with cancer.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".