Risk of hospital-related morbidity among survivors of breast cancer in British Columbia, Canada.
Bibliographic record
Abstract
1551 Background: Long-term consequences of cancer diagnosis and treatment are of increasing concern, as therapeutic interventions improve survival. Various survivorship programs exist; few address the importance of bridging oncology and primary care. This study assesses risk of late hospital-related morbidity among a population-based cohort of 39,436 3-year survivors of female breast cancer in British Columbia, Canada and compares the risk of morbidity to a similarly-aged comparison group. Methods: Demographic and clinical records of breast cancer survivors diagnosed between 1986 and 2005 were linked to inpatient records from provincial administrative databases. A comparison group from the provincial health insurance plan registry, frequency-matched by birth cohort was identified. Morbidity was ascertained from diagnosis codes listed on hospital records, and categorized by organ system. Poisson regression was used to assess the relative risk of morbidity, adjusting for sociodemographic factors. Results: Compared to controls, non-relapsed survivors diagnosed age 18-39 (N=1158) had more than twice the risk of morbidity (RR 2.57, 95%CI 2.21-2.98); those with a relapse (N=580) had eight times the risk (95% CI 6.78-9.44). Among those diagnosed age ≥40, non-relapsed survivors (N=20473) had a 62% increase in risk RR 1.62, 95% CI 1.57-1.68; relapsed survivors (N=5223) had triple the risk of morbidity (RR 2.89, 95% CI 2.78-3.01). In both cohorts, excess risks were statistically significant for most types of non-neoplastic morbidity, with highest rates seen for disorders of the blood, endocrine, skin and circulatory systems. Among survivors, those diagnosed with a higher stage cancer had increased risk of morbidity. Type of treatment received did not correlate with increased risk of morbidity (RR 1.05, 95% CI 1.00–1.11 for combination of surgery, radiation and systemic therapy vs surgery alone); majority of the risk increase is likely related to the impacts of cancer itself rather than treatment. Conclusions: Survivors of breast cancer are at an increased risk of a wide range of morbidities years after diagnosis. This underscores calls for improved models of survivorship care and continued survivorship research.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".