Cancer screening practices of cancer survivors: population-based, longitudinal study.
Bibliographic record
Abstract
OBJECTIVE: To describe cancer screening rates for cancer survivors and compare them with those for matched controls. DESIGN: Population-based, retrospective study with individuals linked across administrative databases. SETTING: Ontario. PARTICIPANTS: Survivors of breast (n = 11 219), colorectal (n = 4348), or endometrial (n = 3473) cancer, or Hodgkin lymphoma (HL) (n = 2071) matched to general population controls. Survivors were those who had completed primary treatment and were on "well" follow-up. The study period was 4 years (1 to 5 years from the date of cancer diagnosis). MAIN OUTCOME MEASURES: Never versus ever screened (in the 4-year study period) for breast cancer, colorectal cancer (CRC), and cervical cancer and never versus ever received (during the study period) a periodic health examination; rates were compared between cancer survivors and controls. Random effects models were used to estimate odds ratios and 95% CIs. RESULTS: Sixty-five percent of breast cancer survivors were never screened for CRC and 40% were never screened for cervical cancer. Approximately 50% of CRC survivors were never screened for breast or cervical cancer. Thirty-two percent of endometrial cancer survivors were never screened for breast cancer and 66% were never screened for CRC. Forty-four percent of HL survivors were never screened for breast cancer, 77% were never screened for CRC, and 32% were never screened for cervical cancer. Comparison with matched controls showed a mixed picture, with breast and endometrial cancer survivors more likely, and CRC and HL survivors less likely, than controls to be screened. CONCLUSION: There is concern about the preventive care of cancer survivors despite frequent visits to both oncology specialists and family physicians during the "well" follow-up period.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".