Frontal Ostium Grade (FOG): A New Computer Tomography Grading System for Endoscopic Frontal Sinus Surgery
Bibliographic record
Abstract
Objective The location and size of the frontal sinus ostium are critical in determining surgical difficulty. The more anterior the ostium, the more difficult is the surgical access. We propose a novel computed tomography (CT) grading specific to the anatomical position of the frontal ostium. Study Design Observational study followed by a prospective part. Settings Tertiary rhinology practice Subject and Methods On a specified sagittal CT cut, a vertical line was drawn through the posterior edge of the frontal process of the maxilla (frontal buttress/beak) along its vertical axis (reference [R‐] line). A second (S‐) line was placed at the point of upturn of the skull base. Based on if the S‐line was posterior or anterior to the R‐line, the frontal ostium was graded positive and more easily accessible or negative and thereby more challenging, respectively. If both lines overlapped, then a neutral (0) grading existed. Results A total of 297 CTs (594 ostia) were analyzed. In total, 394 (65%) ostia were grade positive, 52 (8.75%) were grade negative, and 103 (17.3%) were grade neutral. Ninety frontal sinusotomies were then performed using this grading system: 48 were positive, 21 negative, and 21 neutral. The average time to complete a frontal sinusotomy was 9.96 minutes for grade positive compared to 11.4 minutes for neutral and 16.05 minutes for grade negative (P <. 005). Conclusion This novel anatomical CT grading system is designed to be useful in planning and predicting the level of difficulty in endoscopic frontal sinus surgery.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".