Validation of a grading system for the attachment of the superior turbinate to the sphenoid face
Bibliographic record
Abstract
BACKGROUND: The attachment of the superior turbinate to the sphenoid face may be an important factor in determining the approach for sphenoidotomy. We sought to validate a previously described 4-type grading system for superior turbinate attachment (Type: A, within its medial one-third; B, in its middle one-third; C, to its lateral one-third; and D, directly to the orbit) to the face of the sphenoid sinus and to make recommendations for its use in determining the method of sphenoidotomy (transethmoidal vs transsphenoethmoidal). METHODS: Single-slice images through both sphenoid sinus ostia were obtained from axial series of computed tomography (CT) scans. Eighteen (36 ostia) sets of scans were used. Attachment type (A-D) in each image was classified by 10 experienced sinus surgeons and compared against a "gold standard" grading performed by the senior author (A.J.), who was the developer of the grading system. RESULTS: Mean accuracy was 63% (95% confidence interval [CI], 54%-72%) for the 4-type grading system. When Types A+B and Types C+D were grouped together, mean accuracy was 91% (95% CI, 84%-97%). For the 2-group classification system, bootstrap analysis suggested that 97% of physicians attain an accuracy of at least 80%. CONCLUSION: Accuracy using the 4-type classification is too low to be practically useful. Accuracy using the 2-group system may be sufficiently high to be a useful aid in selecting a surgical approach. We recommend a transethmoid sphenoidotomy for Types A and B and a transsphenoethmoidal approach to the sphenoid for Types C and D.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".