The clinical significance of trigeminal neuralgia nomenclature
Bibliographic record
Abstract
Introduction: Accurate diagnosis and classification of facial pain is critical for appropriate assignment of potential surgical treatments, avoidance of misdirected interventions, and study outcomes. There are however inconsistencies between commonly applied diagnostic criteria, particularly for trigeminal neuralgia. The aim of the present study was to analyze and define the diagnostic elements of the common facial pain conditions presenting to neurosurgery. These common facial pains include typical trigeminal neuralgia, atypical trigeminal neuralgia, idiopathic facial pain, and idiopathic trigeminal neuropathy. Method: We will conduct a retrospective review of general epidemiological, clinical details and long term follow up of Manitoba patients with facial pain diagnoses presenting to the Winnipeg Center for Cranial Nerve Disorders. While such information has previously been well documented for some conditions, there are significant knowledge gaps in regards to conditons such as idiopathic facial pain and trigeminal neuropathy. The wCCND provides exclusive neurosurgery care in Manitoba for patients with facial pain, with approximately 40 new referrals annually, in addition to referrals received from across Canada. A prospectively maintained database of patients will be accessed to identify study subjects from 2001-2015, and clinic charts reviewed to supplement data obtained at initial presentation. Significance: Differentiation of the common facial pain diagnoses has great clinical and research importance. There exist, however, significant controversy in the classification of these conditions. This research expects to provide comprehensive clinical details important in diagnosing and classification of cranial facial pain conditions. The B.Sc. Med student is expected to gain expertise in cranial nerve disorders that will be fostered with mentorship exposure in the neurosurgery clinic, ward, and operating room in addition to literature review and research activities.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".