A Novel International Endoscopic Sphenoid Surgery Classification (IESSC): A Delphi Consensus
Bibliographic record
Abstract
BACKGROUND: Advancements in endoscopic sinus and skull base surgery created a need for standardized terminology to describe sphenoid sinus surgery. Although classification systems exist for other sinuses, one for endoscopic sphenoid sinus surgery is lacking. Developing such a system would standardize procedure descriptions and promote a common language among surgeons. This study aimed to develop a new classification system for endoscopic sphenoid surgery. METHODS: Consensus on a novel endoscopic sphenoid surgery classification system by running the Delphi procedure with 16 rhinology experts from around the world. RESULTS: Four Delphi rounds were required to reach a consensus on all stages of the classification. The average percentage of agreement on the stages of classification progressively increased from 70.83% in the first round to 87.68% in the last round. The rejection rates continuously decreased from 8.81% in the first round to 4.44% in the last round. The classification system was developed as follows: stage 1, presphenoid surgery; stage 2A, partial sphenoidotomy; stage 2B, complete sphenoidotomy; stage 2C, transpterygoid sphenoidotomy; stage 3A, Rostral sphenoidectomy; and stage 3B, extended sphenoid drill-out. CONCLUSIONS: This novel endoscopic sphenoid surgery classification system facilitates the description of different sphenoid sinus procedures, providing surgeons with better opportunities for discussion and communication.
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 | 0.065 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".