Abstract: The Landscape of Hand Surgery Research in Global Health: A Unified Approach to Better Care
Bibliographic record
Abstract
PURPOSE: Since the inception of the Lancet Commission for Global Surgery and the Touching Hands Project, there is escalating interest in international outreach in hand surgery.1,2 Linking evidence-based research with international outreach in resource-limited settings can optimize outcomes in hand surgery.1,3 To date there has been no literature review of hand surgery and global health. This study aims to summarize common themes and gaps in global health-focused hand surgery research so volunteers worldwide can build into the research priorities of local centres. METHODS/MATERIALS: A PRISMA guided scoping review was conducted using PubMed, Embase, African Journal Online (AJOL), the Indian Journal of Plastic Surgery (IJPS), Scholar’s Portal, and the American Journal of Hand Surgery. Search terms included: hand injury, congenital, trauma, burn, infection; “AND” global health, international outreach, poverty, low-middle income country, socioeconomic, and poverty. All peer-reviewed studies conducted until January 1, 2018 were included. A grounded theory approach was then applied, by which themes were updated as the study progressed. Common themes and gaps were summarized. Publications were plotted on an online world map using the platform BatchGeo. RESULTS: Two independent investigators reviewed 853 articles, with 37 articles included. Hand trauma (n=9, 24%), and emphasis on physiotherapy (n=7, 18%), were the most common themes. Congenital anomalies, infections, tumours and socioeconomic pre-disposition followed after (n=4,11%). Common sources of hand trauma were occupation, followed by road accidents and injuries at home. All four hand infection articles focused on tropical diabetic hand syndrome. Targeting prevention (n=7, 18%), developing a hand injury registry (n=4, 11%), and cultivating opportunities for hand surgery education (n=4, 11%) were needs commonly identified in research. The majority of the literature was retrospective (n=8), case report/series or opinion pieces (n=7, 18%). Four papers had international collaborators, of which three were prospective and one was qualitative. India published the most (n=11, 30%) followed by Nigeria (n=5, 14%). Publications from higher-income countries (n=11, 30%) produced literature reviews or reports from personal experience. Limitations include specific focus on North American databases, AJOL and IJPS, as well as exclusion of non-english speaking studies (n=2) and or studies inaccessible due to cost (n=9). CONCLUSION: Research is scattered across multiple databases, inaccessible by additional cost, or non-English speaking regions. There is a need to implement and evaluate trauma prevention strategies in the workplace and to develop a hand injury registry. International research collaboration can lead to higher level evidence. Research plotted on a multi-lingual online world map offers a unified approach for worldwide research collaboration to meet the research priorities of low-resource areas. References: 1. Chung KY. The role for international outreach in hand surgery. Journal of Hand Surgery. 2017 Aug 1;42(8):652–5. 2. Kozin SH. The richness of caring for the poor: the development and implementation of the Touching Hands Project. Journal of Hand Surgery. 2015 Mar 1;40(3):566–75. 3. Birbeck GL, Wiysonge CS, Mills EJ, Frenk JJ, Zhou XN, Jha P. Global health: the importance of evidence-based medicine. BMC medicine. 2013 Dec;11(1):223.
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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 | 0.110 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".