Meta-Analysis of Prediction Models for Trigeminal Neuralgia Recurrence After Surgery

A systematic review and meta-analysis of 20 studies involving 4,291 patients, conducted by Xinxin Tian and published on figshare in May 2026. The work evaluates the performance of models predicting postoperative recurrence of trigeminal neuralgia, reporting pooled area under the curve values of 0.86 for training and 0.83 for validation sets.

Use Cases

• Benchmarking new recurrence prediction models based on the reported pooled AUC metrics.
• Identifying key clinical predictors like age and disease duration for feature engineering in model development.
• Assessing methodological quality and bias in clinical prediction studies based on the review's risk-of-bias findings.

Strengths

• Includes a substantial patient cohort of 4,291 individuals across 20 studies.
• Reports specific performance metrics, including a pooled AUC of 0.86 for training sets.
• Identifies concrete predictors such as age ≥65 years and disease duration >5 years.

Limitations

• The dataset is a 45.5 KB document; the underlying tabular data from the review is not directly provided.
• Row count and column-level documentation for any extracted data are unknown.
• The authors caution that the pooled AUC may be overestimated due to bias and heterogeneity.

Provenance

Source

Systematic review and meta-analysis of studies from eight databases, registered with PROSPERO (CRD420251153545).

Collection Method

Literature search, data extraction following the CHARMS checklist, and meta-analysis using a random-effects model.

Time Range

Literature search conducted up to September 23, 2025.

Freshness

Last updated 2026-05-07 04:45:14.

Geography

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