Preparing for ICD-11 transition: lessons from case studies in Argentina and Mexico
Bibliographic record
Abstract
Objectives: To explore the early stages of the International Classification of Diseases 11th Revision (ICD-11) implementation in Argentina and Mexico, focusing on mortality coding, to identify essential elements and key considerations for successful adoption. Methods: Qualitative analysis was conducted using case studies from Argentina and Mexico. Data were collected through interviews, workshops, and document analysis to uncover opportunities, challenges, and strategic decisions in ICD-11 implementation. Results: Key findings highlight the critical role of comprehensive system assessments, strategic partnerships, financial planning, technological readiness, targeted training initiatives, and structured evaluation mechanisms. Both countries emphasized the importance of tailored strategies aligned with their unique contexts and highlighted the need for collaboration across sectors and the establishment of national task forces. Challenges included navigating the complexities of integrating ICD-11 within existing systems and enhancing interoperability through accelerated development of tools and establishment of expert networks. Conclusions: Tailored strategies are essential for integrating ICD-11 into national health information systems. Greater collaboration, establishment of national task forces, and clear monitoring frameworks are crucial for successful implementation. Guided by digital health and health informatics expertise, countries can overcome challenges and align with broader health care objectives, thereby ultimately enhancing global health outcomes through effective ICD-11 adoption. By learning from early adopters such as Argentina and Mexico, other countries can better prepare for their own transitions to ICD-11.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".