Computed tomography assessment of anterior ethmoidal canal dehiscence: An interobserver agreement study and review of the literature
Bibliographic record
Abstract
PURPOSE: The anterior ethmoidal artery can be injured in functional endoscopic sinus surgery. The ability of computed tomography (CT) to identify dehiscence of the anterior ethmoidal canal (AEC) has not been widely evaluated. The aim of this study was to evaluate the interobserver agreement in the CT assessment of AEC dehiscence. METHODS: We conducted a retrospective review of consecutive CT scans of the paranasal sinuses (PNS) between January 1, 2012, and December 31, 2012. Two neuroradiologists separately assessed the presence of AEC dehiscence, the presence of PNS opacification, and the best CT plane to evaluate the AEC. Statistical analysis included descriptive analysis and interobserver agreement (kappa coefficient). RESULTS: The AEC was below the skull base in 199 (22.3%) cases. Dehiscence of the AEC was found in 13.2% for reader 1 and in 7.3% for reader 2. The interobserver agreement for identification of AEC dehiscence was only fair (κ = 0.246). The interobserver agreement for the AEC dehiscence in cases with opacification of ethmoidal air cells was substantial (κ = 0.754). CONCLUSION: The suboptimal interobserver agreement could potentially limit the usefulness of CT scans for routine assessment of AEC dehiscence. In patients with PNS opacification, CT scans could still add valuable information regarding AEC dehiscence.
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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 | 0.023 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".