P.086 The clinical significance of trigeminal neuralgia nomenclature
Bibliographic record
Abstract
Background: A diagnosis of trigeminal neuralgia (TN) may be broadly applied to many neuralgic facial pains, while more stringent criteria are required for management decisions, outcome assessment, and pathophysiological correlations. Our aim was to evaluate existing classification systems of facial pain. Methods: The study population was comprised of 534 Manitobans referred to neurosurgery for facial pain from 2001 to 2013. A retrospective chart review identified presenting features; pain distribution, nature, and duration. The recorded diagnoses (rDx) were then re-classified according to the International Classification of Headache Disorders (ICHD-3) and Burchiel System of TN1 and TN2. Results: There was complete correlation between rDx and ICHD-3 for typical TN (tTN) in 266(49.8%) patients, atypical TN (aTN) in 39(7.3%), and idiopathic facial pain (IFP) in 59(11%). Idiopathic trigeminal neuropathy (iTn) in 35(6.6%) was not classified in ICHD-3. Burchiel-TN1 included heterogeneous diagnoses including tTN (266), aTN (27), iTn (2) and IFP (8); Burchiel-TN2 included aTN (10), iTn (23), and IFP (15). Another 135(25.5%) had other facial pain diagnoses. Conclusions: Classification of TN is especially important when selecting and evaluating surgical treatments. Diagnostic criteria should clearly differentiate between unique conditions and ideally have basis on underlying etiology. The ICHD-3 nomenclature best satisfies these aims although should be expanded to include iTn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.015 | 0.004 |
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".