Challenges in traumatic spinal cord injury care in developing countries – a scoping review
Bibliographic record
Abstract
Objective: To evaluate the leading challenges in developing countries' traumatic spinal cord injury (TSCI) care. Methods: We conducted a systematic search in electronic databases of PubMed, SCOPUS, Web of Science, EMBASE, and Cochrane Library on 16 April 2023. Studies that investigated challenges associated with the management of TSCI in developing countries were eligible for review. We extracted related outcomes and categorized them into four distinct parts: injury prevention, pre-hospital care, in-hospital care, and post-hospital care. Results: We identified 82 articles that met the eligibility criteria including 13 studies on injury prevention, 25 on pre-hospital care, 32 on in-hospital care, and 61 on post-hospital care. Challenges related to post-hospital problems including the personal, financial, and social consequences of patients' disabilities and the deficiencies in empowering people with TSCI were foremost studied. Lack of trained human resources, insufficient public education and delays in care delivery were barriers in the acute and chronic management of TSCI. A well-defined pre-hospital network and standard guidelines for the management of acute neurotrauma are needed. Critical challenges in injury prevention include deficiencies in infrastructure and supportive legislation. Conclusion: Studies focusing on injury prevention and pre-hospital care in TSCI management in developing countries warrant further investigation. It is imperative to develop systematic and evidence-based initiatives that are specifically tailored to the unique circumstances of each country to address these challenges effectively. By understanding the primary obstacles, policymakers and healthcare providers can establish goals for improving education, planning, legislation, and resource allocation.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".