Optical Coherence Tomography Abnormalities as the Presenting Sign of an Involuted Sellar/Suprasellar Mass
Bibliographic record
Abstract
Introduction: Pituitary adenomas are benign tumours that can lead to visual loss through compression of the optic chiasm. Patients with pituitary adenomas often present with visual field defects (commonly bitemporal hemianopia), but some may be asymptomatic. In such cases, abnormalities may only be detected through visual field testing or optical coherence tomography (OCT) of the ganglion cell-inner plexiform layer (GCIPL), which may provide a more sensitive method for detecting such abnormalities. Case Presentation: A 72-year-old man was incidentally found to have binasal OCT-GCIPL thinning during a routine eye examination. Visual acuity was 20/20 in both eyes. Pupils were equal and reactive without a relative afferent pupillary defect. His Humphrey 24-2 SITA-Fast visual field test results were normal. A magnetic resonance imaging (MRI) revealed a nonenhancing (cystic) sellar/suprasellar mass measuring 1.7 cm craniocaudal by 2.1 cm anteroposteriorly, without associated optic chiasm compression. The lesion was suspected to be either a cystic pituitary adenoma or a Rathke's cleft cyst. Follow-up examination 1 year later showed all findings remained stable, including an unchanged visual acuity, visual fields, OCT-GCIPL, and MRI. Conclusion: The binasal thinning observed on OCT-GCIPL in this case, despite the absence of chiasmal compression on MRI, is suggestive of previous compression of the optic chiasm. This case highlights the potential for spontaneous regression of pituitary adenomas and underscores the importance of OCT-GCIPL as a vital tool for detecting optic chiasmal damage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".