Management of persistent idiopathic facial pain (PIFP) – An international Delphi study
Bibliographic record
Abstract
Background/AimPersistent idiopathic facial pain (PIFP) is a rare condition with a lifetime prevalence of approximately 0.03%. It is characterized by persistent daily facial pain without identifiable cause and presents diagnostic and therapeutic challenges due to unknown pathophysiology, symptom overlap with other painful disorders, and limited evidence-based treatments. The aim of this Delphi study was to establish international consensus-derived guidelines for the management of patients with PIFP.MethodsA three-round Delphi study was conducted with 16 international pain experts, each with ≥10 years of clinical experience in pain management and extensive peer-reviewed publications. The first round involved open-ended questions, and the qualitative data were analyzed using systematic text condensation, resulting in a quantitative questionnaire with 42 statements. Subsequent rounds employed Likert-scale responses to these statements. Consensus was defined as ≥80% agreement or disagreement. In addition, if 11-12 (68-75 percent) out of the 16 experts agreed or disagreed, consensus was not reached, but a majority was considered to have a particular opinion.ResultsConsensus was reached in 35 out of the 42 statements (83%), emphasizing multidisciplinary collaboration and avoidance of invasive procedures in the treatment of PIFP. In an additional three statements (7%) a majority of the experts agreed with each other. In four statements (10%), no consensus or majority was reached. Pharmacological treatments, including tricyclic antidepressants, serotonin-norepinephrine reuptake inhibitors, and gabapentinoids, may be considered; however, opioids should generally be avoided in the treatment of PIFP. Patient education and behavioral therapies are important interventions, and the most important measure of therapeutic success is improved quality of lifeConclusionThe present Delphi study has established internationally derived consensus guidelines and recommendations for the evaluation and comprehensive management of patients with PIFP. This is a first step in gathering knowledge for future evidence-based guidelines and more specific treatment recommendations. These international expert consensus guidelines recommend a multi- or interdisciplinary approach in managing PIFP, avoiding invasive interventions and prioritizing patient-centered outcomes.
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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 | 0.068 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".