Stratification of surgical outcomes in trigeminal neuralgia using multimodal data
Bibliographic record
Abstract
Abstract Chronic pain remains a challenge for clinicians, with limited individualized predictive tools that can aid with diagnosis, disease course, or prediction of treatment outcomes. We hypothesized that a comprehensive analysis, encompassing a patient's complete pain-related clinical data, medical history and brain imaging, can identify key contributors linked to surgical outcomes and stratify specific outcome categories for trigeminal neuralgia (TN)—chronic facial pain syndrome. Using supervised and unsupervised machine learning approaches, we analysed data from 102 subjects with classical TN. Pre-surgical clinical data were processed through unsupervised learning to delineate key clinical contributors of TN outcome stratification and their correlation with surgical response. Concurrently, we applied supervised learning to pre-surgical T1-weighted brain magnetic resonance imaging. Clinical data analysis uncovered pain and non-pain-related measures—including pain frequency, degree of medication relief, pain character, presence of diabetes and cancer history—as the most significant in forecasting surgical outcome. Analysis revealed strong correlation of pre-surgical clinical data with surgical response duration (r = 0.5, P < 0.00001). Imaging data analysis used a support vector machine classification model with high recall for subjects who would be either long-term responders or non-responders 0.79 and 0.86 with the area under the receiver operating characteristic curve (AUC) of 0.86 and 0.84, respectively. The average multiclass accuracy in predicting the duration of surgical response categories was 78% (AUC 0.8). Together, these results show that TN surgical outcome categories are distinguishable, and surgical outcome can be stratified based on combined clinical and brain imaging data available prior to surgical treatment. We suggest a novel perspective on different strata of chronic pain disorders, each with structural imaging, clinical correlates and specific surgical outcomes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".