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Abstract: The Landscape of Hand Surgery Research in Global Health: A Unified Approach to Better Care

2018· article· en· W2893067044 on OpenAlexaff
Karen Y. Chung, Taeyoung Hong, Andrew Howard, Christopher R. Forrest

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2018
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOutreachGlobal healthMedicinePovertySocioeconomic statusHealth careFamily medicineHand surgeryMedical educationNursingSurgeryPolitical sciencePublic healthEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.363
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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