Travel-Related Illnesses and Conditions
Bibliographic record
Abstract
RESULTS:A total of 173 unique journals with 47,227 unique RCTs, of which 43,322 (92%) had age-specific categorization were included; 28,991 (67%) were RCTs with only adults enrolled, 5,895 (14%) were RCTs with only children enrolled, and the remaining 8,436 (19%) were categorized as RCTs with both adults and children enrolled.Adult RCTs increased by an average of 90.5 RCTs/yr (95% CI: 78-103), which was significantly higher (p<.0001) than pediatric RCTs which rose by 16.9 RCTs/yr (95% CI: 11-22) or RCTs involving both children and adults which rose by 22.7 RCTs/yr (95% CI: 10-35).79% (23/29) of specialties demonstrated a greater rise in the number of published RCTs/yr involving adults than those enrolling children.A greater rise in RCTs/yr in children compared with adults was found in immunology and tropical medicine; no difference in secular trends between adult and childhood age groups was found for allergy, behavioral sciences, microbiology, and toxicology.CONCLUSION: Adult RCT publications are increasing at a faster rate than pediatric RCTs in almost all specialties.If RCTs are considered the gold standard for assessing safety and efficacy of interventions, this has implications for the quality of evidence-based care delivered to children.
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 | 0.015 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".