A systematic review of screening diagnostic tools for trigeminal neuralgia
Bibliographic record
Abstract
Background and objective: Trigeminal neuralgia (TN) is a rare chronic neuropathic pain condition of sudden and severe pain, often described as an electric shock. Diagnosis is challenging for non-expert clinicians, particularly in primary care settings. We wanted to identify and assess the diagnostic accuracy of existing screening tools for TN and orofacial pain that could be used to support the diagnosis of TN in primary care. Databases and data treatment: We searched key databases (MEDLINE, ASSIA, Embase, and Web of Knowledge and PsycINFO) supplemented by citation tracking from January 1988 to 2021. We used an adapted version of the Quality of Diagnostic Accuracy Studies (QUADAS-2) to assess the methodological quality of each study. Results: Searches identified five studies, from the UK, USA and Canada; three validated self-report questionnaires; and two artificial neural networks. All screened for multiple orofacial pain diagnoses, including dentoalveolar pain, musculoskeletal pain (temporomandibular disorders) and neurological pain (trigeminal neuralgia, headache, atypical facial pain and postherpetic neuralgia). The overall quality assessment was low for one study. Conclusions: Diagnosis of TN can be challenging for non-expert clinicians. Our review found few existing screening tools to diagnose TN, and none is currently suitable to be used in primary care settings. This evidence supports the need to adapt an existing tools or to create a new tool for this purpose. The development of an appropriate screening questionnaire could assist non-expert dental and medical clinicians to identify TN more effectively and empower them to manage or refer patients for treatment more effectively.
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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 | 0.018 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".