Pathum Raksa Project: Addressing Disparity in Breast Cancer Care Through National Innovation in Thailand
Bibliographic record
Abstract
Supinda Koonmee,1 Ongart Somintara,2 Piyapharom Intarawichian,1 Chaiwat Aphivatanasiri,1 Sakkarn Sangkhamanon,1 Suphawat Laohawiriyakamol,3 Rujira Panawattanakul,4 Phanchanut Mahantassanapong,5 Chayanoot Rattadilok,6 Piyarat Jeeravongpanich,7 Wilart Krongyute,8 Krisada Prachumrasee,9 Reza Alaghehbandan10 1Department of Pathology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand; 2Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand; 3Division of General Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand; 4Department of Surgery, Udonthani Hospital, Udonthani, Thailand; 5Department of Anatomical Pathology, Surin Hospital, Surin, Thailand; 6Department of Surgery, Nopparatrajathanee Hospital, Bangkok, Thailand; 7Anatomical Pathology Unit, Songkhla Hospital, Songkhla, Thailand; 8Department of Surgery, Fort Suranari Hospital, Nakhon Ratchasima, Thailand; 9The College of Local Administration, Khon Kaen University, Khon Kaen, Thailand; 10Department of Pathology, Faculty of Medicine, University of British Columbia, Royal Columbian Hospital, Vancouver, BC, CanadaCorrespondence: Chaiwat AphivatanasiriDepartment of Pathology, Faculty of Medicine, Khon Kaen University, Khon Kaen, ThailandEmail chaiap@kku.ac.thReza AlaghehbandanUniversity of British Columbia, Royal Columbian Hospital, Vancouver, BC, CanadaEmail reza.alagh@gmail.comPurpose: 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: The rate of triple-negative breast cancers in KKU and Udonthani decreased 52.8% (p = 0.02) and 28.9% (p = 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.Keywords: breast cancer, biomarkers, Pathum Raksa, multidisciplinary teams, pre-analytical phase
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".