Healthcare delivery for non-communicable diseases among breast cancer survivors in Sri Lanka: Is there a missed opportunity?
Bibliographic record
Abstract
PURPOSE: Breast cancer is the most common cancer globally as well as in Sri Lanka. Improvements in cancer care have allowed patients to live to an older age. With advancing age, incidence of non-communicable diseases (NCDs) increases. Cancer diagnoses tend to take attention away from the treatment of other comorbidities, given its importance. The objective of this study was to describe healthcare delivery for NCDs among female breast cancer survivors treated at the National Cancer Institute of Sri Lanka (NCISL) and identify opportunities to optimise non-cancer medical care in this cohort. METHODS: A total of 420 women were identified from the breast cancer database at the NCISL, who were 50-80 years at the time of their breast cancer diagnosis, were within 12-24 months from the date of diagnosis, had completed their active cancer treatment and were in complete remission. Of this population, 228 (54%) women who had documented NCDs at the time of diagnosis were identified and were followed-up via telephone to collect details regarding existing comorbidities and the screening and development of new comorbidities. RESULTS: At the time of cancer diagnosis, 216/228 (95%) of patients had hypertension, 104/228 (46%) had type 2 diabetes and 17/228 (8%) had ischaemic heart disease (IHD). The prevalence of other comorbidities was very low. During the post diagnosis period, 11 patients developed type 2 diabetes, while 2 developed IHD. Osteoporosis screening using dual-energy X-ray absorptiometry scanning was very low at diagnosis 21/228 (9%) but improved in post cancer treatment follow-up 112/228 (49%, p < 0.001). Only 95/228 (42%) were screened for other cancers. CONCLUSIONS: Hypertension was the most prevalent comorbidity while type 2 diabetes and dyslipidaemia were the most common diagnoses post-treatment. In these patients, screening for osteoporosis and other cancers remains very low, emphasising a missed opportunity.
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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 | 0.000 | 0.000 |
| 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 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".