Meningioma in patients exposed to progestin drugs: results from a real-life screening program
Bibliographic record
Abstract
Abstract Purpose: To report the results of systematic meningioma screening program implemented by French authorities in patients exposed to progestin therapies (cyproterone (CPA), nomegestrol (NA), and chlormadinone (CMA) acetate).Methods: We conducted a prospective monocentric study on patients who, between September 2018 and April 2021, underwent standardized MRI (injection of gadolinium, then a T2 axial FLAIR and a 3D-T1 gradient-echo sequence) for meningioma screening.Results: Of the 210 included patients, 15 (7.1%) had at least one meningioma; seven (7/15, 47%) had multiple meningiomas. Meningiomas were more frequent in older patients and after exposure to CPA (13/103, 13%) compared to NA (1/22, 4%) or CMA (1/85, 1%; P=0.005). After CPA exposure, meningiomas were associated with longer treatment duration (median=20 vs 7 years, P=0.001) and higher cumulative dose (median=91 g vs. 62 g, P=0.014). Similarly, their multiplicity was associated with higher dose of CPA (median=244 g vs 61 g, P=0.027). Most meningiomas were ≤1 cm3 (44/58, 76%) and were convexity meningiomas (36/58, 62%). At diagnosis, patients were non-symptomatic, and all were managed conservatively. Among 14 patients with meningioma who stopped progestin exposure, meningioma burden decreased in 11 (79%) cases with no case of progression during MR follow-up.Conclusion: Systematic MR screening in progestin-exposed patients uncovers small and multiple meningiomas, which can be managed conservatively, decreasing in size after progestin discontinuation. The high rate of meningiomas after CPA exposure reinforces the need for systematic screening. For NA and CMA, further studies are needed to identify patients most likely to benefit from screening.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".