The Role of Developing Countries in Generating Cochrane Meta-analyses in the Field of Pediatrics (Neonatology and Neuropediatrics): A Systematic Analysis
Bibliographic record
Abstract
BACKGROUND: There is a lack of up-to-date, systematic reviews that critically assess the role and potential limitations of evidence-based medicine and systematic Cochrane reviews originating in developing countries. METHODS: We performed a systematic literature review of all Cochrane reviews published between 1997 and 2010 by the Cochrane Neonatal Review Group (CNRG) in the field of neuropediatrics. The main outcome parameter of our review was the assessment of the percentage of reviews that originated in developing countries and the number of reviews that provided conclusive/ inconclusive data. RESULTS: In total, 262 reviews were performed in the field of neonatology and 112 in the field of neuropediatrics. Only a small fraction (15/262 in neonatology [7/15 conclusive] and 16/112 in neuropediatrics [9/16 conclusive]) originated in developing countries. CONCLUSIONS: There is an ongoing need for high-quality research that addresses specific issues that are most relevant to the medical care of children in developing countries. Funding and research agencies will play a pivotal role in selecting the most appropriate research programs for the developing world.
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 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 | 0.343 | 0.653 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.041 |
| Bibliometrics | 0.033 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".