SAT-083 Overutilization of Surveillance Neuroimaging for Stable Microprolactinomas in Alberta
Bibliographic record
Abstract
Abstract Disclosure: F. Mulji: None. J. Mann: None. B. Manoranjan: None. K. Lithgow: None. Background: Microprolactinomas are the most common type of pituitary adenoma with prevalence of ∼40 per 100,000. Medical therapy with dopamine agonist (DA) is first line treatment for symptomatic microprolactinomas. Neuroimaging with MRI sella is standard of care for imaging of microprolactinomas. Though guidelines suggest performing follow-up MRI sella one year after starting DA, a recent study of macroprolactinomas found no significant increases in tumour volume following normalization of prolactin over a mean follow-up of ten years, demonstrating that MRI surveillance in this setting is unnecessary. These findings present an opportunity to decrease health care resource utilization, particularily in microprolactinomas which are not associated with mass effect. We hypothesize that MRI sella is overutilized in the surveillance of stable microprolactinomas at our centre. Our primary aim is to assess practice patterns with respect to timing and frequency of surveillance neuroimaging for stable DA-treated microprolactinomas in our health region. Methods: This is a retrospective study inclusive of cases from 2003-2022. Potentially eligible cases were extracted by a data analyst. Inclusion criteria are 1) age > 18 years and 2) diagnosis of microprolactinoma (prolactin secreting adenoma <1 cm). Clinical variables for each eligible case were extracted including baseline tumour characteristics, baseline and follow-up prolactin, management strategy (DA vs. conservative management) and dates and results of all surveillance MRIs. Each case was reviewed to determine if there was an appropriate clinical indication for each MRI including 1) rising prolactin 2) neurological symptoms or concern for apoplexy 3) for planning surgery or radiation. Results: Initial data extraction yielded 1977 cases. Thus far, 213 cases have been screened, of which 20 meet our inclusion criteria (n=17 women, median age 32, IQR 5). Most cases (n=18) were managed initially with DA (n=11 cabergoline; n=9 bromocriptine). Median follow-up interval was 144 months (IQR 87) during which time a median of 4 surveillance MRIs were performed. A total of 89 surveillance MRIs were performed for all cases during the follow-up period, of which 73 (82%) did not have an appropriate clinical indication. Conclusion: Surveillance neuroimaging for stable microprolactinomas is overutilized at our centre. Further data extraction and analysis of this cohort is ongoing. Oure preliminary results illustrate opportunity to reduce healthcare costs and resource utilization. Presentation: Saturday, July 12, 2025
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".