ArticleIrish journal of medical science2026
Differentiating septic arthritis from non-infectious inflammatory causes of acute monoarticular arthritis in children: A machine learning approach based on routine laboratory tests.
Article in Irish journal of medical science, 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAcute monoarthritis in children poses a diagnostic challenge, particularly in distinguishing septic arthritis from non-infectious inflammatory causes. Delayed or incorrect diagnosis may lead to serious complications or inappropriate treatment. This study aims to develop and validate machine learning (ML) models for distinguishing septic arthritis from non-infectious inflammatory arthritis in children presenting with acute monoarthritis, using routinely available laboratory markers including body temperature, conventional inflammatory markers (CRP and ESR), and complete blood count (CBC) parameters along with derived hematologic ratios.
methodsWe retrospectively analyzed data from 129 pediatric patients, including 66 with non-septic arthritis and 63 with septic arthritis. Conventional inflammatory markers and CBC-derived ratios (e.g., neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, red cell distribution width-to-platelet ratio) were collected. The dataset was split into training (80%) and test (20%) sets. Five supervised ML algorithms-Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), AdaBoost, and Gradient Boosting-were trained using stratified tenfold cross-validation. Diagnostic performance was assessed using area under the ROC curve (AUC) and feature importance analysis.
resultsThe Random Forest model achieved the highest diagnostic accuracy with an AUC of 0.97 (95% CI: 0.911-1.000), outperforming other models and individual inflammatory markers. Among the input features, neutrophil count, CRP, and several derived ratios were consistently ranked among the most important predictors. Multivariate analysis supported the predictive value of these hematologic indices.
conclusionMachine learning models based on routine CBC parameters can aid in distinguishing septic arthritis from non-infectious causes of acute monoarthritis in children. These results support the integration of ML-based tools into emergency care workflows to facilitate earlier and more accurate clinical decision-making.
Indexed as
Identifiers
42467382What Socratic 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 Socratic graph.