The Utility of Magnetic Resonance Imaging in Assessing Patients With Pituitary Tumors Compressing the Anterior Visual Pathway
Bibliographic record
Abstract
BACKGROUND: Pituitary tumors are one of the most common types of intracranial neoplasms, and can cause progressive visual loss. An ongoing challenge in the management of patients with pituitary tumors is the cost, availability, and reliability of current magnetic resonance imaging (MRI) techniques to capture clinically significant incremental tumor growth. The purpose of this study was to evaluate the various MRI-based structural analyses and to explore the relationship between measures of structure and function in the afferent visual pathway of patients with pituitary tumors. METHODS: We performed a critical review of literature on MRI-based structural analyses of pituitary adenomas using PubMed, Embase, Cochrane Library, and Google Scholar. In addition, preoperative structural characteristics of the optic apparatus, optic nerve compression, and optic chiasm elevation identified as important in the literature review, were examined in 18 of our patients from October 2010 to January 2014. RESULTS: In our review of literature, a total of 443 citations were obtained from our search strategy and review of bibliographies. Eight of these studies met inclusion/exclusion criteria and were retrieved for critical review. Of the 8 included studies, only 2 studies examined the relationship between MRI-based structural measurements and postoperative visual recovery. In our small case-series, MRI analysis of chiasm elevation, severity of optic nerve compression, chiasm position, height of chiasm, tumor height, and tumor volume failed to differentiate patients with postoperative visual dysfunction vs those with visual recovery (P > 0.05). CONCLUSIONS: Although MRI-based structural analysis is an important and useful tool for managing patients with pituitary tumors, there are limited objective measures shown to be predictive of postoperative visual recovery.
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".