The Impact of Pre-analytical Quality Initiatives on Cholangiocarcinoma Diagnostics in Thailand
Bibliographic record
Abstract
Cholangiocarcinoma (CCA) is the most prevalent malignancy in Thailand, with unfortunate late diagnosis and frequent metastatic disease outcomes. An accurate tissue diagnosis is the first and most important step in the treatment of CCA. Tissue quality and preservation during the pre-analytical phase play major roles in the proper histological evaluation and potential biomarker testing. This study evaluated the impact of using the "Cholangiocarcinoma Screening and Care Program (CASCAP)" container, as an innovative tool to address pre-analytical challenges faced by pathology laboratories in Thailand. This is a comparison study examining the quality of CCA specimens using the CASCAP container vs. the conventional method, using hematoxylin and eosin (H&E) and immunohistochemistry (IHC). CCA tissue quality using the CASCAP container significantly reduced artifact deposition while improving the cellular structure and nuclear and cytoplasmic morphologies. The immunohistochemical expression of cytokeratin 19 (CK19), a prognostic marker in CCA, significantly improved in the CASCAP container group in comparison with the conventional method. This innovation is proven to significantly enhance the CCA tissue quality diagnostics and prognostic biomarker testing, hence improving overall cancer care, diagnosis, and treatment in Thailand.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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 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".