ReviewThe journal of headache and pain2024
Machine learning classification meets migraine: recommendations for study evaluation.
Review in The journal of headache and pain, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers, 2 of them syntheses that pooled it.
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
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Exhaled breath volatile organic compounds (VOCs) detection methods: GC-MS versus eNose in COPD diagnosis-a systematic review and meta-analysis.BMC pulmonary medicine · 2025Pooled it
- Application of machine learning in migraine classification: a call for study design standardization and global collaboration.The journal of headache and pain · 2025Pooled it
- Oscillatory edge connectivity in pain-related regions supports machine learning identification of migraine.The journal of headache and pain · 2026Article
- Unmasking the noise: aberrant cortical oscillations in visual snow syndrome.The journal of headache and pain · 2026Article
- Functional Magnetic Resonance Imaging for Investigating the Role of the Hippocampus in Migraine with Aura.Diagnostics (Basel, Switzerland) · 2026Article
- Central executive network-related neuromagnetic abnormalities in chronic and episodic migraine: a resting-state magnetoencephalography study.Frontiers in neuroscience · 2026Article
- Temporal stability and neural complexity in resting-state MEG predict migraine phenotypes.The journal of headache and pain · 2025Article
- Interpretable Artificial Intelligence Analysis of Functional Magnetic Resonance Imaging for Migraine Classification: Quantitative Study.JMIR medical informatics · 2025Article
- A nomogram for the prediction of response to anti-CGRP mAbs: the CGRP score.The journal of headache and pain · 2025Article
- Artificial neural networks applied to somatosensory evoked potentials for migraine classification.The journal of headache and pain · 2025Article
- Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 2.The journal of headache and pain · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classification of migraine types and subtypes. However, inconsistencies in study design, lack of methodological transparency, and the absence of external validation limit the impact and reproducibility of such studies. This paper presents a framework of six essential recommendations for evaluating ML-based classification in migraine research: (1) group homogenization by clinical phenotype, attack frequency, comorbidity, therapy, and demographics; (2) defining adequate sample size; (3) quality control of collected and preprocessed data; (4) transparent training, testing, and performance evaluation of ML models, including strategies for data splitting, overfitting control, and feature selection; (5) interpretability of results with clinical relevance; and (6) open data and code sharing to facilitate reproducibility. These recommendations aim to balance the trade-off between model generalization and precision while encouraging collaborative standardization across the ML and headache communities. Furthermore, this framework intends to stimulate discussion toward forming a consortium to establish definitive guidelines for ML-based classification research in migraine field.
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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.