Medical missions for the provision of paediatric cardiac surgery in low- and middle-income countries
Bibliographic record
Abstract
This review will outline the role of visiting cardiac surgical teams in low- and middle-income countries drawing on the collective experience of the authors in a wide range of locations. Requests for assistance can emerge from local programmes at a beginner or advanced stage. However, in all circumstances, careful pre-trip planning is necessary in conjunction with clinical and non-clinical local partners. The clinical evaluation, surgical procedures, and postoperative care all serve as a template for collaboration and education between the visiting and local teams in every aspect of care. Education focusses on both common and patient-specific issues. Case selection must appropriately balance the clinical priorities, safety, and educational objectives within the time constraints of trip duration. Considerable communication and practical challenges will present, and clinicians may need to make significant adjustments to their usual practice in order to function effectively in a resource-limited, unfamiliar, and multilingual environment. The effectiveness of visiting trips should be measured and constantly evaluated. Local and visiting teams should use data-driven evaluations of measurable outcomes and critical qualitative evaluation to repeatedly re-assess their interim goals. Progress invariably takes several years to achieve the final goal: an autonomous self-governing, self-financed, cardiac programme capable of providing care for children with complex CHD. This outcome is consistent with redundancy for the visiting trips model at the site, although fraternal, professional, and academic links will invariably remain for many years.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".