Nasofrontal Outflow Tract Visibility in Computed Tomography Imaging of Frontal Sinus Fractures
Bibliographic record
Abstract
The choice of frontal sinus fracture treatment is based on multiple factors, one of which is injury to the nasofrontal outflow tract (NFOT). Computed tomography (CT) imaging of the NFOT can play an important role in the decision process. We sought to assess the visibility of the NFOT on CT scans in frontal sinus fractures. Patients with frontal sinus fractures (including the posterior table) receiving a CT scan from April 1st 2001 to December 31st 2009 were included. Scans were retrospectively assessed for available views (axial, coronal, and sagittal), slice thickness, inclusion of the anatomical NFOT region in the scanned area, and visibility of the NFOT. A total of 170 patients were included. In majority (71%) of patients NFOT was visible on one or more views, whereas in 33% (N = 56) of patients had three complete views (complete anatomical NFOT region scanned in three views). In this subgroup, the ability to assess the NFOT increased to 89%. When selecting patients with three complete views of ≤ 2 mm slice thickness (N = 47), the ability to assess the NFOT increased to 96%. In conclusion, when assessing the NFOT using CT imaging, having three complete views (axial, coronal, and sagittal) and a ≤ 2 mm slice thickness greatly increases the NFOT visibility.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".