The humanitarian aid of neurosurgical missions in Peru: A chronicle and future perspectives
Bibliographic record
Abstract
Background: The unmet neurosurgical need has remained patent in developing countries, including Peru. However, continuous efforts to overcome the lack of affordable care have been achieved, being neurosurgical missions one of the main strategies. We chronicle the humanitarian labor of organizations from high-income countries during their visit to Peru, the contributions to local trainees' education, and the treatment of underserved patients. Furthermore, we discuss the embedded challenges from these missions and the future perspective on long-term partnerships and sustainability. Methods: This is a narrative review. We searched the literature in PubMed and Google Scholar about neurosurgical missions conducted in Peru. Results: Since 1962, twelve organizations from high-income countries have delivered humanitarian help in Peru by training local neurosurgeons, treating low-income patients, and providing surgical instrumentation. Out of the three main regions of Peru, cities on the coast and highlands have hosted most of these missions, with no reported outreach in the amazon area. About 75% of the organizations are headquartered in the United States, followed by Canada, Luxembourg, and Spain. In addition, 50% of the organizations have an active partnership. The predominant focus of these missions has been pediatrics, neuro-oncology, and spine surgery. Conclusion: Neurosurgical missions have represented a strategy to close the disparity in education and treatment in Peru. However, additional efforts must be conducted to improve long-term partnership and sustainability, such as adopting standardized indicators for progress tracking, incorporating remote technologies for continuous training and communication, and expanding partnerships in less attended areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".