ArticleBMC musculoskeletal disorders2026
Development and validation of natural language processing pipelines to extract injury and surgery-related data elements from ACL reconstruction operative reports.
Article in BMC musculoskeletal disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOver 100,000 patients undergo ACLR each year in the United States, providing rich electronic medical record data to improve outcomes, but manual chart review can be time-consuming and expensive. Natural language processing (NLP) methods have been successfully used to extract information for other orthopaedic operative procedures (e.g., hip and knee arthroplasties). Different subspecialities, however, use diverse terminology or "sublanguages" to describe clinical concepts and NLP models perform better concept extraction when trained on specialty-specific texts. Therefore, the purpose of this study was to develop and validate a reliable NLP pipeline to extract meaningful clinical data elements (e.g., graft type, meniscal involvement) from ACLR operative reports.
methodsOperative reports for a training and test set were randomly selected based on surgeon volume and year from individuals who underwent ACLR within a single healthcare system between 2013 and 2021. Clinical Language Annotation, Modeling, and Processing Toolkit (CLAMP) was utilized to train a domain-specific model and build a pipeline for data extraction of clinically meaningful data elements, including injury-related factors and surgical factors. Relevancy metrics were calculated using values identified by a single clinician as the gold standard.
resultsOverall, the individuals selected for the training and test sets (n = 437 total) were 26.6 ± 10.9 years old, and 43.5% were female, similar to the age (26.7 ± 11.5 years) and proportion of females (43.0%) in the full data set (n = 5,818). Priority entities, including the side of surgery, ACL graft type, ACL procedure, and meniscal involvement, were identified with F1 scores between 0.87-1.
conclusionsF1 scores of 0.87-1 were attained for several priority entities using a modest set of annotated ACL operative reports to train the model and an NLP tool designed for non-expert use. The NLP pipelines have the potential to extract relevant operative information for a large cohort of patients to support clinical research, such as identifying predictors of subsequent surgery after ACLR. This work can also inform the planning of future studies using larger training sets and more advanced NLP methods.
Indexed as
Identifiers
What 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.