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Record W2747297738 · doi:10.4314/mmj.v29i2.23

Trauma care in Malawi: A call to action

2017· article· en· W2747297738 on OpenAlexaff
Wakisa Mulwafu, Linda Chokotho, Nyengo Mkandawire, Hemant Pandit, Dan Deckelbaum, Chris Lavy, Kathryn H. Jacobsen

Bibliographic record

VenueMalawi Medical Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineCall to actionRehabilitationHealth carePublic healthMedical emergencyOccupational safety and healthInjury preventionDeveloping countryPoison controlNursingPhysical therapyEconomic growth

Abstract

fetched live from OpenAlex

Injuries are a global public health concern because most are preventable yet they continue to be a major cause of death and disability, especially among children, adolescents, and young adults. This enormous loss of human potential has numerous negative social and economic consequences. Malawi has no formal system of prehospital trauma care, and there is limited access to hospital-based trauma care, orthopaedic surgery, and rehabilitation. While some hospitals and research teams have established local trauma registries and quantified the burden of injuries in parts of Malawi, there is no national injury surveillance database compiling the data needed in order to develop and implement evidence-based prevention initiatives and guidelines to improve the quality of clinical care. Studies in other low- and middle-income countries (LMICs) have demonstrated cost-effective methods for enhancing prehospital, in-hospital, and post-discharge care of trauma patients. We encourage health sectors leaders from across Malawi to take action to improve trauma care and reduce the burden from injury in this country.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.057
GPT teacher head0.384
Teacher spread0.327 · 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.

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".

Quick stats

Citations34
Published2017
Admission routes1
Has abstractyes

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