Sphenoid sinus septations: unpredictable anatomic landmarks in endoscopic pituitary surgery.
Bibliographic record
Abstract
OBJECTIVE: We sought to determine whether sphenoid sinus septations could be used as predictable landmarks to identify the internal carotid artery prominence. METHODS: Fifty-six preoperative, high-resolution computed tomographic scans were identified between January 2007 and December 2009 on patients undergoing endoscopic transsphenoidal pituitary tumour resection. The number and termination locations of sphenoid sinus septations were noted, and their relationship to the internal carotid artery prominence was studied. RESULTS: In this series, each sphenoid sinus contained a mean of 1.57 septations. We analyzed 88 sphenoid sinus septations and found only 17% inserting at either internal carotid artery prominence. CONCLUSION: In our study, the presence of sphenoid sinus septations could not be reliably used as a surgical landmark to predict the location of the internal carotid artery. Our article stands in contrast to other literature on this topic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".