Risk factors for adverse drug reactions in pediatric inpatients: a systematic review
Bibliographic record
Abstract
BACKGROUND: The main objective of the present systematic review is to identify potential risk factors for adverse drug reactions (ADRs) through prospective cohort studies in pediatric inpatients. METHODS: The data search was done in the following electronic databases PubMed/MEDLINE; Scopus; LILACS and Web of Science from the earliest record until 31 May 2015. Two reviewers independently screened each study and one of them assessed the methodological quality according to the Newcastle-Ottawa scale for cohort studies. The data extraction was conducted according to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative for cohort studies. RESULTS: The only risk factor observed in all studies was the increase in the number of prescription drugs. However, other factors were identified, such as the increase in the length of stay or the number of low- or high-risk drugs prescribed, use of general anesthesia and oncological diagnosis. The cumulative incidence of ADR was 16.4% (95% confidence interval: 15.6 to 17.2). The main professional responsible for ADR identification was the pharmacist and the dominant category among the ADRs were gastrointestinal disorders. In addition, analgesics, antibacterial agents and corticosteroids were the drug classes commonly associated with ADRs. The methodology used in this study was tried to homogenize the data extracted; however, this was not sufficient to correct the discrepancies so it was not possible to perform a meta-analysis. CONCLUSIONS: The increase in the number of prescription drugs was the main risk factor in this population. However, additional studies are required to identify the risk factors for ADRs in pediatric inpatients.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".