Comparison of Sonography and Computed Tomography as Imaging Tools for Assessment of Airway Structures
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to compare airway anatomic parameters as measured by sonography and computed tomography (CT). METHODS: Fifteen adult patients underwent CT followed by sonography of the anterior neck under standard conditions. A radiologist and an anesthesiologist with experience in airway imaging examined the scans and performed measurements of specific airway parameters: distance to the posterior surface of the tongue, thickness of the submental region, hyomental distance, depth of the epiglottis from skin (above and below the hyoid bone), thyrohyoid distance, depth of the arytenoid cartilage from skin, and fat pad thickness at the thyroid cartilage. After performing the measurements, they compared the images by the two modalities for descriptions of the structures. Means and SDs were calculated for the measurements, and a paired t test was performed to determine statistically significant differences in the measurements by sonography and CT. RESULTS: The means of all parameters were closely related except hyomental distance (sonography, 5.23 ± 0.58 cm; CT, 3.50 ± 0.42 cm). The paired t test showed that the mean values for depth of the epiglottis below the hyoid (3.89 versus 4.17 cm; P = .31), thyrohyoid distance (1.03 versus 1.02 cm; P = .95), and depth of the arytenoid cartilage (2.90 versus 2.66 cm; P = .21) were not significantly different as measured by sonography and CT, respectively. CONCLUSIONS: The study shows that sonography can reliably image all of the structures visualized by CT, and in general, infrahyoid parameters agree well between the two modalities, as opposed to suprahyoid parameters, which may be affected by unintentional head extension.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".