Quantitative Volumetric Analysis Post Transsphenoidal Pituitary Adenoma Surgery
Bibliographic record
Abstract
BACKGROUND: Computed tomogram (CT) imaging is often used for immediate postoperative assessment of transsphenoidal pituitary adenoma resection while magnetic resonance imaging (MRI) is used for follow-up. The residual mass is known to decrease in size over time but the difference between the two imaging modalities has not been quantified. Our objective was to quantify the size difference of the residual mass on immediate postoperative CT compared with delayed MRI. METHODS: Retrospective analysis of 69 patients who had undergone pituitary adenoma resection at our institution between 2004-2010. Sellar and suprasellar diameter, along with the overall volume of the residual mass were measured on both the immediate postoperative CT and delayed MRI. RESULTS: Average preoperative sellar and suprasellar diameter was 22.2 ± 4.6mm and 20.9 ± 5.9mm, respectively. Average sellar residual diameter on immediate postoperative CT (16.5 ± 5.4 mm, 25% reduction) was significantly larger than delayed MRI (10.6 ± 6.2mm, 52% reduction). The average suprasellar component on CT (15.5±6.5mm, 26% reduction) was also significantly larger than that on MRI (3.3 ± 5.4 mm, 84% reduction). The postoperative CT showed a 46% reduction in volume while a 71% reduction was noted on the delayed MRI. CONCLUSION: A significant reduction in residual mass is noted on delayed MR imaging compared with immediate postoperative CT. Therefore, from a resource management and prognostication point of view, CT should be used for immediate postoperative assessment while delayed MRI should be used to assess operative success and for communication with patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".