The Positive and Negative Predictive Value of Targeted Diagnostic Botox Injection in Nerve Decompression Migraine Surgery
Bibliographic record
Abstract
BACKGROUND: Nerve decompression surgery is an effective treatment modality for patients who experience migraines. Botulinum toxin type A (Botox) injections have been traditionally used as a method to identify trigger sites; however, there is a paucity in data regarding its diagnostic efficacy. The goal of this study was to assess the diagnostic capacity of Botox in successfully identifying migraine trigger sites and predicting surgical success. METHODS: A sensitivity analysis was performed on all patients receiving Botox for migraine trigger site localization followed by a surgical decompression of affected peripheral nerves. Positive and negative predictive values were calculated. RESULTS: A total of 40 patients met our inclusion criteria and underwent targeted diagnostic Botox injection followed by a peripheral nerve deactivation surgery with at least 3 months' follow-up. Patients with successful Botox injections (defined as at least 50% improvement in Migraine Headache Index scores after injection) had significantly higher average reduction in migraine intensity (56.7% versus 25.8%; P = 0.020), frequency (78.1% versus 46.8%; P = 0.018), and Migraine Headache Index (89.7% versus 49.2%; P = 0.016) postsurgical deactivation. Sensitivity analysis shows that the use of Botox injection as a diagnostic modality for migraine headaches has a sensitivity of 56.7% and a specificity of 80.0%. The positive predictive value is 89.5% and the negative predictive value is 38.1%. CONCLUSIONS: Diagnostic targeted Botox injections have a very high positive predictive value. It is therefore a useful diagnostic modality that can help identify migraine trigger sites and improve preoperative patient selection. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, II.
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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 | 0.006 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".