Transforming Plastic and Reconstructive Surgical Care in Low- and Middle-Income Countries: A Paradigm Shift to the Diagonal Model
Bibliographic record
Abstract
Summary: The article highlights the global lack of access to basic surgical services, particularly in low- and middle-income countries (LMICs), where only 3.5% of surgical procedures serve 34.8% of the population. Plastic and reconstructive surgery, constituting 16% of treatable conditions, is a significant unmet need. Surgical outreach, popular for burns, trauma, and cleft lip, is addressed by organizations like ReSurge, Smile Train, and Operation Smile. The shift from the traditional “vertical model” to a “diagonal model” prioritizes long-term relationships, capacity-building, and sustainable healthcare. Efforts include education through programs like the ReSurge Global Training Program, a blended learning approach, and technology integration for ongoing support. The diagonal model aims to address not just immediate patient needs but also systemic challenges, emphasizing collaboration and empowerment for sustainable healthcare outcomes.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".