ICAR: endoscopic skull‐base surgery
Bibliographic record
Abstract
BACKGROUND: Endoscopic skull-base surgery (ESBS) is employed in the management of diverse skull-base pathologies. Paralleling the increased utilization of ESBS, the literature in this field has expanded rapidly. However, the rarity of these diseases, the inherent challenges of surgical studies, and the continued learning curve in ESBS have resulted in significant variability in the quality of the literature. To consolidate and critically appraise the available literature, experts in skull-base surgery have produced the International Consensus Statement on Endoscopic Skull-Base Surgery (ICAR:ESBS). METHODS: Using previously described methodology, topics spanning the breadth of ESBS were identified and assigned a literature review, evidence-based review or evidence-based review with recommendations format. Subsequently, each topic was written and then reviewed by skull-base surgeons in both neurosurgery and otolaryngology. Following this iterative review process, the ICAR:ESBS document was synthesized and reviewed by all authors for consensus. RESULTS: The ICAR:ESBS document addresses the role of ESBS in primary cerebrospinal fluid (CSF) rhinorrhea, intradural tumors, benign skull-base and orbital pathology, sinonasal malignancies, and clival lesions. Additionally, specific challenges in ESBS including endoscopic reconstruction and complication management were evaluated. CONCLUSION: A critical review of the literature in ESBS demonstrates at least the equivalency of ESBS with alternative approaches in pathologies such as CSF rhinorrhea and pituitary adenoma as well as improved reconstructive techniques in reducing CSF leaks. Evidence-based recommendations are limited in other pathologies and these significant knowledge gaps call upon the skull-base community to embrace these opportunities and collaboratively address these shortcomings.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".