What If All Patients with Breast Cancer in Malaysia Have Access to the Best Available Care: How Many Deaths Are Avoidable?
Bibliographic record
Abstract
BACKGROUND: Cancer is a leading cause of death in the world and the fourth leading cause in Malaysia. A widening disparity in cancer burden has emerged between high and low-middle income countries. A similar disparity due to differential access to cancer care between affluent and deprived groups is likely to exist within developing country too. We assess this inequality by estimating the number of deaths due to cancer that would be avoidable if all patients had access to the best available care in Malaysia, a high middle income country.METHODS: The number of avoidable deaths is the difference between the number of deaths estimated by GLOBOCAN12 for Malaysia (which is consistent with published estimates on cancer survival), and the expected number of deaths if all patients with Breast Cancer (BC) had experienced the age-ethnic-stage specific survival outcomes observed in a leading private cancer centre in Malaysia. Data on age-ethnic-stage composition of the general BC population were from local cancer registry and public hospitals providing safety net cancer services.FINDINGS: Of the 2312 excess deaths due to BC, 2048 (88%) were avoidable. Of these avoidable deaths, 1167 (57%) were attributable to late stage presentation while 881 (43%) were due to lack of access to optimal treatment. Sensitivity analyses however show that the 88% avoidable deaths may be as low as 50%, taking into account differences in socio-economic status, over-diagnosis and lack of very long term survival data.INTERPRETATION: The huge number of avoidable deaths highlights the high cancer mortality rate among the deprived and the vast disparity in access to cancer care between the rich and poor within Malaysia, which mirrors the global cancer divide between rich and poor countries.Cancer care system that deliver such disastrous and inequitable outcomes is clearly under-performing. It is in urgent need of reform.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".