ArticlePediatric research2026
Differences in bleeding outcome capture between electronic health record review using natural language processing and ICD-10 coding in hospitalised children.
Article in Pediatric research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Natural Language Processing (NLP) tools are superior to ICD-10 codes in capturing clinically relevant information.Pediatric research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
impactNLP applied to paediatric EHRs identified substantially more clinically documented bleeding events than ICD-10 coding. Demonstrates considerable loss of clinically assessed bleeding information when relying on administrative datasets alone. Shows that narrative-based ascertainment captures the full spectrum of documented bleeding events not transferred to structured coding. Provides a scalable method for documentation-based bleeding surveillance in hospitalised children. Supports use of NLP to improve outcome ascertainment in research, safety monitoring, and quality registries.
Identifiers
42056540What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.