Does published research on non-communicable disease (NCD) in Arab countries reflect NCD disease burden?
Bibliographic record
Abstract
OBJECTIVES: To review trends in non-communicable (NCD) research output in the Arab region, in terms of quantity and quality, study design, setting and focus. We also examined differences by time and place, and assessed gaps between research output and NCD burden. METHODS: A scoping review of a total of 3,776 NCD-related reports published between 2000 and 2013 was conducted for seven Arab countries. Countries were selected to represent diverse socio-economic development levels in the region: Regression analyses were used to assess trends in publications over time and by country. Research gaps were assessed by examining the degree of match between proportionate literature coverage of the four main NCDs (CVD, cancer, DM, and COPD) and cause-specific proportional mortality rates (PMR). RESULTS: The annual number of NCD publications rose nearly 5-fold during the study period, with higher income countries having the higher publication rates (per million populations) and the most rapid increases. The increase in the publication rate was particularly prominent for descriptive observational studies, while interventional studies and systematic reviews remained infrequent (slope coefficients = 13.484 and 0.883, respectively). Gap analysis showed a mismatch between cause-specific PMR burden and NCD research output, with a relative surplus of reports on cancer (pooled estimate +38.3%) and a relative deficit of reports on CVDs (pooled estimate -30.3%). CONCLUSION: The widening disparity between higher and lower-income countries and the discordance between research output and disease burden call for the need for ongoing collaboration among Arab academic institutions, funding agencies and researchers to guide country-specific and regional research agendas, support and conduct.
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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 | 0.048 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.027 | 0.039 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".