Patterns of health care-related multimorbidity among breast cancer survivors.
Bibliographic record
Abstract
115 Background: Breast cancer survivors may experience multiple health conditions, both pre-existing and related to the cancer and its treatment. The objective of this study is to evaluate the total burden of healthcare-related morbidity and multimorbidity among breast cancer survivors, in the first five years from diagnosis. Methods: A population-based cohort of 6,944 female five-year breast cancer survivors, diagnosed at 18 years or older from 2000-2003, was identified from the Cancer Registry of the province of British Columbia, Canada, and followed to end 2008. Subjects were linked to all their provincial healthcare records of hospitalizations and outpatient physician service claims, excluding oncology and emergency visits, from diagnosis to 5 years post-diagnosis. International Classification of Disease (ICD) diagnostic codes for “reason for service” were extracted, then assigned to one of 32 diagnosis groups (Aggregated Diagnosis Groups (ADGS)) of the Johns Hopkins Adjusted Clinical Group (ACG) System. The ACG system aggregates diagnoses with similar expected demand for healthcare, primarily based on level of severity and persistence of disease. ADG distributions were generated. Results: Only 380(5.5%) of the survivors were < 40 years old at diagnosis; 1670(24.1%) were > 70 years. The majority (5648(81.3%)) were Stage I or II. In the 5 year period from diagnosis, most patients had at least one type of morbidity, with 47% having 11-15 different types of morbidities. Over 90% of survivors had reports of major ( = very high expected healthcare resource use) signs or symptoms, 3258 (46.9%) survivors experienced major adverse effects/injuries, 3232 (46.5%) had unstable chronic medical conditions, and 2696 (38.8%) had major time-limited infections. Approximately 49.3% had stable or unstable psychosocial conditions. Conclusions: Breast cancer patients experience extensive multimorbidity within five years of diagnosis, with high expected healthcare utilization. These results highlight the importance of comprehensive multimorbidity assessment, with care recommendations, included in cancer survivorship care plans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".