Extracapsular Pituitary Macroadenoma Resection and Internal Carotid Artery Aneurysm Clipping via Endoscopic Endonasal Approach: Technical Description
Bibliographic record
Abstract
Pituitary macroadenomas and internal carotid artery parasellar aneurysms are relatively common conditions that occur rarely in the same patient. All skull base surgeons should be aware of the potential of this dangerous and challenging coincidence, and have strategies for safe and effective treatment of both conditions. We present a case of a 53-year-old woman who began noticing a progressively deteriorating peripheral visual field in her left eye for ∼6 to 8 months. Subsequently, she began noticing rapid decline in her right eye vision, prompting assessment at our institution. A longstanding history of recurrent mild headaches was also obtained. A large sellar and suprasellar mass, consistent with a pituitary macroadenoma, causing displacement of the optic apparatus was found on CT imaging. In addition, concern of an intracranial internal carotid artery (ICA) aneurysm was raised based on subtle MRI findings. A subsequent CT angiogram confirmed the presence of a parasellar left ICA aneurysm. Due to the rapid progression of the patient’s visual loss in her right eye and 2 to 3 months’ time frame for preoperative anticoagulation therapy prior to endovascular treatment for her aneurysm, prompt surgical intervention was considered. This patient was taken to the operative room and underwent extracapsular dissection of her pituitary macroadenoma to expose the neck of the aneurysm. The aneurysm was clipped via endoscopic endonasal approach at the same sitting. Pre- and postoperative imaging, as well as intraoperative images and videos are shown to illustrate the diagnostic and therapeutic nuances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".