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Record W2788150862 · doi:10.1186/s13643-018-0700-2

Strategies for successful trauma registry implementation in low- and middle-income countries—protocol for a systematic review

2018· review· en· W2788150862 on OpenAlexaff
Tiffany Paradis, Etienne St‐Louis, Tara Landry, Dan Poenaru

Bibliographic record

VenueSystematic Reviews · 2018
Typereview
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsMedicineData extractionProtocol (science)Software deploymentMedical emergencyGrey literatureMEDLINEAlternative medicinePathologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: The benefits of trauma registries have been well described. The crucial data they provide may guide injury prevention strategies, inform resource allocation, and support advocacy and policy. This has been shown to reduce trauma-related mortality in various settings. Trauma remains a leading cause of mortality in low- and middle-income countries (LMICs). However, the implementation of trauma registries in LMICs can be challenging due to lack of funding, specialized personnel, and infrastructure. This study explores strategies for successful trauma registry implementation in LMICs. METHODS: The protocol was registered a priori (CRD42017058586). A peer-reviewed search strategy of multiple databases will be developed with a senior librarian. As per PRISMA guidelines, first screen of references based on abstract and title and subsequent full-text review will be conducted by two independent reviewers. Disagreements that cannot be resolved by discussion between reviewers shall be arbitrated by the principal investigator. Data extraction will be performed using a pre-defined data extraction sheet. Finally, bibliographies of included articles will be hand-searched. Studies of any design will be included if they describe or review development and implementation of a trauma registry in LMICs. No language or period restrictions will be applied. Summary statistics and qualitative meta-narrative analyses will be performed. DISCUSSION: The significant burden of trauma in LMIC environments presents unique challenges and limitations. Adapted strategies for deployment and maintenance of sustainable trauma registries are needed. Our methodology will systematically identify recommendations and strategies for successful trauma registry implementation in LMICs and describe threats and barriers to this endeavor. SYSTEMATIC REVIEW REGISTRATION: The protocol was registered on the PROSPERO international prospective register of systematic reviews ( CRD42017058586 ).

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 imitation

Not 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.

metaresearch head score (Codex)0.111
metaresearch head score (Gemma)0.149
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.111
Threshold uncertainty score0.587

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1110.149
Meta-epidemiology (narrow)0.0060.006
Meta-epidemiology (broad)0.0160.017
Bibliometrics0.0150.013
Science and technology studies0.0040.005
Scholarly communication0.0090.011
Open science0.0060.007
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0720.010

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 head0.127
GPT teacher head0.467
Teacher spread0.340 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

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".

Quick stats

Citations33
Published2018
Admission routes1
Has abstractyes

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