Review of Non-Communicable Disease Research Activity in Kuwait: Where is the Evidence for the Best Practice?
Bibliographic record
Abstract
BACKGROUND: Kuwait, a small country in the Middle East, is now facing rapid development, with non-communicable diseases (NCDs) accounting for the majority of deaths. OBJECTIVES: In this study, we review trends in NCD research productivity in Kuwait and examine to what extent it is aligned with disease burden. METHODS: Systematic mapping of NCD papers produced between January 2000 and December 2013 yielded 893 publications. These were defined according to study design, study focus, and risk factors examined. Research gaps were assessed by examining disparities between literature produced and cause-specific proportional mortality rates (PMR) and disability-adjusted life years (DALYs). FINDINGS: While annual publication rates increased more than two-fold during the study period, many of the study methodologies were descriptive (58%). Only 2.6% were based on high-evidence interventional studies. Cancer, CVD, and diabetes featured in 38.1%, 15.1%, and 9.2% of the publications, respectively. Compared to PMR and DALYs, there was a surplus of cancer research, most of which were laboratory-based studies (27.6%) or of the case-report/case-series study type (26.5%). Smoking was more likely to be addressed in relation to CVD (32.6%) than diabetes (6.1%) or cancer (2.1%). Physical inactivity was mostly examined in its relation to diabetes (14.6%), with negligible representation in the remaining study focus (range 0.3% to 2.2%). CONCLUSION: NCD research production in Kuwait is not aligned with disease burden or health priorities. We recommend a coordinated action between funding agencies, universities, and researchers in Kuwait to guide development of a comprehensive research agenda that is responsive to the country's emerging needs.
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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 | 0.028 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.030 | 0.035 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".