Classification of facial pain: a 13-year population-based study
Bibliographic record
Abstract
Introduction: Accurate diagnosis and classification of facial pain is critical for assigning surgical treatment, avoiding misdirected interventions and studying outcomes. We conducted a population-based longitudinal study of patients with facial pain and compared diagnostic classification systems. Methods: Medical records for all Manitobans presenting to our centre with a primary complaint of facial pain from 2001 to 2013 were reviewed. We then applied diagnostic criteria from the International Classification of Headache Disorders (IHS-3), the International Association for the Study of Pain (IASP) and Burchiel’s system for comparisons. Results: There were 534 patients with facial pain (3.4/100,000/year) and two-thirds of these had conditions potentially amenable to neurosurgical interventions. Our most common diagnoses were typical trigeminal neuralgia(50%), atypical trigeminal neuralgia(7%), idiopathic trigeminal neuropathy(7%), idiopathic facial pain(11%); average ages were 65±14(22-99), 60±18(32-86), 55±16(28-83) and 48±12(28-82) with a female proportion of 55%, 59%, 65% and 80%, respectively. Other classification systems included no criteria for idiopathic trigeminal neuropathy. The classifications of “trigeminal neuralgia type-1 and type-2” did not differentiate between surgical and non-surgical candidiates. Conclusion: Published classification systems of facial pain have differing criteria for diagnosis of trigeminal neuralgia and none defines a large group with idiopathic trigeminal neuropathy. This may lead to considerable variability in determinations of potential surgical candidates and comparing outcomes of treatment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".