Different Scale, Different Pain? Discordant Pain Measurements After Surgery for Trigeminal Neuralgia
Bibliographic record
Abstract
OBJECTIVE: Trigeminal neuralgia (TN) has been described as one of the worst pains known to humankind. However, pain severity in TN has been measured using several different scales, resulting in difficulty comparing illness burden and response to TN surgery across studies. We examined the degree of concordance between standardized scales evaluating pain severity in a cohort of patients undergoing surgery for TN. METHODS: In this cross-sectional study, we evaluated 39 surgical TN patients with 3 pain measurement instruments: a Visual Analog Pain Scale, the Brief Pain Inventory-Facial (BPI-F) Pain, and the Barrow Neurological Institute (BNI) Pain Intensity Score. Scores were transformed into a 0-10 scale, and grouped into 5 severity categories (none, mild, moderate, severe, and worst). Discordant patients were those classified in different severity categories by at least 2 pain measurement instruments. Level of agreement was assessed with the intraclass correlation coefficient. RESULTS: Almost 50% of patients (18/39) had at least 1 categorical discordance when comparing all 3 scores. We found 30% discordance between visual analog scale (VAS) and BPI-F, 33% discordance between BPI-F and BNI, and 35% discordance between VAS and BNI. The highest degree of discordance between BNI and either VAS or BPI-F occurred in patients with moderate pain (BNI IIIb). The degree of agreement across all 3 scores was moderate (intraclass correlation coefficient = 0.72). CONCLUSIONS: TN patients with residual mild-moderate pain after surgery are often discordantly classified by different pain measurement scales. These findings argue for a more standardized method of reporting postoperative pain outcomes in the TN literature.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".