A review of teleradiology in Africa – Towards mobile teleradiology in Nigeria
Bibliographic record
Abstract
eHealth is promoted as a means to strengthen health systems and facilitate universal health coverage. Sub-components (e.g. telehealth, telemedicine, mhealth) are seen as mitigators of healthcare provider shortages and poor rural and remote access. Teleradiology (including mobile teleradiology), widespread in developed nations, is uncommon in developing nations. Decision- and policy-makers require evidence to inform their decisions regarding implementation of mobile teleradiology in Nigeria and other sub-Saharan countries. To gather evidence, Scopus and PubMed were searched using defined search strings (September 2020). Duplicates were removed, and titles and abstracts reviewed using specified selection criteria. Full-text papers of selected resources were retrieved and reviewed against the criteria. Insight from included studies was charted for eight a priori categories of information: needs assessment, implementation, connectivity, evaluation, costing, image display, image capture and concordance. Fifty-seven articles were identified, duplicates removed and titles and abstracts of remaining articles reviewed against study criteria. Twenty-six papers remained. After review of full-texts, ten met the study criteria. These were summarised, and key insights for the eight categories were charted. Few papers have been published on teleradiology in sub-Saharan Africa. Teleradiology, including mobile teleradiology, is feasible in sub-Saharan Africa for routine X-ray support of patients and healthcare providers in rural and remote locations. Former technical issues (image quality, transmission speed, image compression) have been largely obviated through the high-speed, high-resolution digital imaging and network transmission capabilities of contemporary smartphones and mobile networks, where accessible. Comprehensive studies within the region are needed to guide the widespread introduction of mobile teleradiology.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".