Pathum Raksa Project: Addressing Disparity in Breast Cancer Care Through National Innovation in Thailand
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Breast cancer is a growing public health challenge in Thailand. Pathum Raksa project was launched in 2015, as a result of higher than expected rate of triple-negative breast cancers in Thai women. The purpose of this project was to identify the cause(s) and address the issue(s), hence improving the quality of breast cancer biomarker testing in Thailand. MATERIALS AND METHODS: Nineteen hospitals across the country, with 902 breast cancer patients were enrolled in this study during 2015-2020. The pre- and post-data from Pathum Raksa initiative was only available for Khon Kaen University (KKU) and Udonthani hospitals in Northeast Thailand. We developed a resource-stratified strategic plan that included designing a unique specimen container, forming multidisciplinary teams from the Surgery and Pathology Departments, and employing locally developed innovative technologies to optimize the entire process of breast cancer diagnostics and biomarker testing. RESULTS: = 0.48), respectively. The rate of ER+ breast cancers in both hospitals increased 5% post-Pathum Raksa implementation. The rate of HER2-neu+ (score 3+) also increased in both hospitals (particularly an increased 65% rate in KKU). Luminal A/B cancers were the most common subtype in both KKU and Udonthani hospitals. CONCLUSION: Pathum Raksa project has significantly improved breast cancer biomarker testing in Thailand. As a result of this national innovation, false-negative rates of breast biomarkers have significantly decreased, resulting in improving prognosis, treatment, and survival of breast cancer women in Thailand.
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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) | 0.000 | 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 it