P.130 Secondary trigeminal autonomic cephalgia due to pituitary adenomas: systematic search of the literature and case illustration
Bibliographic record
Abstract
Background: Trigeminal autonomic cephalgias (TAC) have been implicated in a multitude of intracranial mass lesions, including pituitary adenomas. Treatment of the associated pituitary adenoma has been reported to result in resolution of the secondary TAC. Methods: We conducted systematic search of the literature in accordance with PRISMA guidelines using Ovid Medline, Pubmed, Scopus, and Web of Science for TACs with associated pituitary lesions. We searched for all the relevant cases to detail the epidemiology, clinical phenotype of TAC, characteristics of the associated pituitary lesion including its hormonal profile, type of clinical intervention, and outcome. Results: Together with our case, 50 cases are reviewed. Prolactinomas were the most common hormone-producing adenomas (n=35; 71%). Few cases were non-functioning (n=3; 6%). Macroadenomas were predominant (n=30, 70%). Cavernous sinus extension was frequent (n=21; 54%). Medical intervention was the most employed form (n=31; 63%). Surgical intervention was carried in several cases (n=18; 37%). The success rate of medical management was high (n=25/31; 81%). Dopaminergic agonist therapy was the most common medical intervention (n=28; 90%). Conclusions: Secondary TACs with associated pituitary adenomas tend to have debilitating symptoms. The pathophysiology of the association is not entirely clear. Identifying the cause and employing the proper intervention is important to avoid unnecessary suffering.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".