Comparative Audit of Clinical Research in Pediatric Neurology
Bibliographic record
Abstract
Clinical research involves direct observation or data collection on human subjects. This study was conducted to evaluate the profile of pediatric neurology clinical research over a decade. Trends in pediatric neurology clinical research were documented through a systematic comparative review of articles published in selected journals. Eleven journals (five pediatric neurology, three general neurology, three general pediatrics) were systematically reviewed for articles involving a majority of human subjects less than 18 years of age for the years 1990 and 2000. Three hundred thirty-five clinical research articles in pediatric neurology were identified in the 11 journals for 1990 and 398 for 2000, a 19% increase. A statistically significant increase in analytic design (21.8% vs 39.5%; P = .01), statistical support (6% vs 16.6%; P < .0001), and multidisciplinary team (69.9% vs 87%; P = .003) was observed. In terms of specific study design, a significant decline in case reports (34.3% vs 10.3%; P < .0001) and an increase in case-control studies (11.3% vs 22.9%; P = .02) were evident over the 10-year interval. This comparative audit revealed that there has been a discernible change in the methodology profile of clinical research in child neurology over a decade. Trends apparently suggest a more rigorous approach to study design and investigation in this field.
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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 | 0.269 | 0.497 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.031 | 0.043 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".