The application of McGill pain questionnaire in diagnosis and treatment of trigeminal neuralgia
Bibliographic record
Abstract
Objective: To observe the application of McGill pain questionnaire (MPQ) in discrimination and diagnosis of trigeminal neuralgia and assessment of therapeutic effect of radiofrequency thermocoagulation (RFT) on trigeminal neuralgia. Method: There were 159 trigeminal neuralgia patients enrolled in this study, of which 136 classic trigeminal neuralgia (CTN) patients and 23 mixed trigeminal neuralgia (MTN) patients. MPQ was used to identify the pain, and observe the pain relieving after RFT in 124 patients of which. Result: The mean present pain index (PPI) of CTN group was 4.20±0.34, and that of MTN group was 3.50±0.57 (P0.001); In pain rating index(PRI)-sensory subscale, the patients with CTN reported higher pain intensity in terms of sensory experience than patients with MTN (P0.001); The PRI-affective and PRI-evaluative dimensions of pain were significantly different between two groups, the scores were higher in CTN group (P 0.001). Overall, 103 (93.6% ) patients in CTN group responded to RFT with higher immediate improvements in self -report. In MTN group, only 10(58.8%) patients in MTN group responded to RFT with significant improvements in self-report. Conclusion: The application of MPQ could discriminate different type of trigeminal neuralgia well. In view of the different effectiveness in two type of trigeminal neuralgia after RFT, MPQ played an important role in diagnosis, discrimination and therapeutic effect assessment of trigeminal neuralgia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".