Cancer screening and life expectancy of Canadian patients with kidney failure
Bibliographic record
Abstract
BACKGROUND: Patients with end-stage renal disease (ESRD) have at least the same prevalence of breast and cervical cancer but a reduced life expectancy compared with the general population. Whereas cancer screening has been found to be effective in the general population, competing risks in ESRD patients may obviate any screening benefit in this population. The purpose of this study was to determine if patients with ESRD benefit, in terms of life expectancy, by screening for breast and cervical cancer. METHODS: The ESRD mortality data from the Canadian Organ Replacement Registry was combined with North American statistics for breast and cervical cancer mortality, incidence, and screening efficacy for 40-, 60-, and 70-year-old women. A validated method of calculating life expectancy, the declining exponential approximation of life expectancy (DEALE), was used to estimate the average life expectancy with and without screening. The benefit of screening is then the estimated difference in the life expectancy with and without mammography or PAP smears. RESULTS: Without screening, the maximum reduction in life expectancy would be 12 days for 60-year-old women with breast cancer. The maximum calculated benefit from screening was an increase in life expectancy of only 3 days with PAP smears for 60-year-old women. CONCLUSIONS: Breast and cervical cancer screening, in women with ESRD, is not associated with as large a gain in life expectancy as for women of the general population. This conclusion does not necessarily apply to the individual woman with multiple risk factors for breast or cervical cancer and few comorbidities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".