Systematic Review of Surgical Literature from Resource‐Limited Countries: Developing Strategies for Success
Bibliographic record
Abstract
BACKGROUND: Injuries and surgical diseases are leading causes of global mortality. We sought to identify successful strategies to augment surgical capacity and research endeavors in low-income countries (LIC's) based on existing peer-reviewed literature. METHODS: A systematic review of literature from or pertaining to LIC's from January 2002 to December 2011 was performed. Variables analyzed included type of intervention performed, research methodology, and publication demographics such as surgical specialty, partnerships involved, authorship contribution, place and journal of publication. FINDINGS: A total of 2049 articles met the inclusion criteria between 2002 and 2011. The two most common study methodologies performed were case series (44%) and case reports (18%). A total of 43% of publications were without outcome measures. Only 21% of all publications were authored by a collaboration of authors from low-income countries and developed country nationals. The five most common countries represented were Nepal (429), United States (408), England (170), Bangladesh (158), and Kenya (134). Furthermore, of countries evaluated, Nepal and Bangladesh were the only two with a specific national journal. INTERPRETATION: Based on the results of this research, the following recommendations were made: (1) Describe, develop, and stimulate surgical research through national peer-reviewed journals, (2) Foster centers of excellence to promote robust research competencies, (3) Endorse partnerships across regions and institutions in the promotion of global surgery, and (4) Build on outcome-directed research.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".