Addressing quality and safety in anatomic pathology in low- and middle-income countries
Bibliographic record
Abstract
The World Health Organization (WHO) has created a sustainable development goal of reducing preventable mortality from cancer in low- and middle-income countries (LMICs) by 30% by 2030. Central to achieving this goal is the creation and maintenance of quality anatomic pathology services (APS). Within the last decade, quality assurance programs and patient safety measures have become a major focus of research for upper middle- and high-income countries (UMHICs), which has led to marked documented improvement in the quality of services provided by laboratories, as well as a decrease in patient safety events. We propose that as APS are developed in LMICs, the lessons learned by UMHICs are necessary to incorporate to produce quality and safe services toward obtaining the aforementioned goal. Furthermore, data suggests that Quality Improvement work requires change at the macrosystems and microsystems levels to achieve these goals. Here, we propose five "microsystems" strategies for professional organizations, healthcare institutions in LMICs and UMHICs that would accelerate quality improvement programs/systems implementation in APS in LMICs.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".