Sex differences in patient journeys to diagnosis, referral, and surgical treatment of trigeminal neuralgia: implications for equitable care
Bibliographic record
Abstract
OBJECTIVE: Trigeminal neuralgia (TN) is an orofacial pain disorder that is more prevalent in females than males. Although an increasing number of studies point to sex differences in chronic pain, how sex impacts TN patients' journeys to care has not been previously addressed. This study sought to investigate sex differences in patients' journeys to diagnosis, referral, and treatment of TN within a large national context. METHODS: Patients with classic TN (n = 100; 50 females and 50 males) were randomly selected through chart reviews at the largest surgical treatment center for TN in Canada for a cross-sectional study. Statistical tests, including Welch's t-test, the chi-square test, Pearson's correlations, and analyses of covariance, were conducted with Python. RESULTS: Key discrepancies between sexes in access to care were identified. Females had a significantly longer referral time interval (average 53.2 months vs 20.4 months, median 27.5 months vs 11.0 months, p = 0.018) and total time interval (average 121.1 months vs 67.8 months, median 78.0 months vs 45.2 months, p = 0.018) than males, despite reporting higher pain intensity at referral. Although medically intolerant patients had a significantly shorter referral time interval than medically tolerant patients (average 13.0 months vs 41.0 months, median 6.0 months vs 17.0 months, p < 0.001), medically tolerant females had a significantly longer referral time interval than medically tolerant males (average 59.9 months vs 21.7 months, median 30.0 months vs 12.0 months, p = 0.017). No statistically significant differences were detected between the sexes for diagnostic time interval (average 63.3 months vs 43.0 months, median 24.0 months vs 24.0 months, p = 0.263) or treatment time interval (average 4.6 months vs 4.7 months, median 4.0 months vs 3.0 months, p = 0.986). CONCLUSIONS: Critical sex differences in patients' journeys to TN surgical treatment were identified, with females enduring considerably longer referral timelines and expressing significantly greater pain intensity than males at referral. Taken together, our findings suggest the presence of unconscious bias and discrimination against females and highlight the need for expediting TN treatment referral for female TN patients.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".