Planum-Clival Angle Classification: A Novel Preoperative Evaluation for Sellar/Parasellar Surgery
Bibliographic record
Abstract
Objective Endonasal approaches are increasingly used to treat sellar pathologies, leading to increased interest in achieving maximal safe resection. We propose a tool-the planum-clival angle (PCA)-and explore its surgical implications for sellar pathology resections. Design Retrospective analysis. Participants Consecutive patients with pituitary lesions between 2003 and 2013. Outcome Measures The PCA and suprasellar extension ratios; head position and extent of surgical resection. Results We enrolled 89 patients (ages 21-88 years). There were 15 type A patients (17%), 13 with suprasellar extension (89%) and ratios between 0.12 and 0.70. There were 61 type B patients (70%), 49 with suprasellar extension (81%) and ratios from 0.09 to 0.66. Finally, there were 13 type C patients (13%), 10 with suprasellar extension (73%) and ratios from 0.21 to 0.76. Type B was treated with a sphenoidectomy and neutral head positioning, type A with 10 to 20 degrees of flexion and an additional posterior ethmoidectomy with or without posterior planum resection, and type C with 10 to 20 degrees of extension and an additional superior clival resection. Conclusions Sellar anatomy and PCA influence the growth patterns of sellar lesions. Thus PCA should allow for better surgical planning and thereby improve surgical efficacy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".