Evaluation of sella turcica types with two different classifications in cone-beam computed tomography
Bibliographic record
Abstract
Introduction: Sella turcica is a saddle-shaped depression in the sphenoid bone that contains the pituitary gland.Objectives: To investigate the morphological types of sella turcica with two different classifications in conebeam computed tomography (CBCT) images in a group of Turkish population.Material and methods: Full head CBCT images of 1,000 patients (535 men, 465 women), aged 6-91 years, were retrospectively analyzed.Morphological types of sella turcica were evaluated in multiplanar planes of CBCT according to two classification systems, and a relationship between them was examined.Relationships between categorical variables were analyzed using c 2 test.Results: Frequency of basic morphological types of sella turcica was as follows: oval, 48%; flattened, 34.7%; circular, 17.3%.Flattened type of basic morphological sella types was significantly higher in males (p = 0.001).Except this type, there was no significant difference between genders.Normal type of sella turcica was detected in 532 (53.2%) of 1,000 patients.Frequency of other morphological types of sella turcica were as follows, respectively: irregularity (notching) in the posterior part of dorsum sella, 40.9%; sella turcica bridging, 2%; pyramidal shape of dorsum sella, 1.7%; oblique anterior wall, 1.2%; double contour of sellar floor, 1%.Irregularity (notching) in the posterior part of sella turcica type was observed significantly more in cases, in which basic morphological sella types were flattened (p = 0.001).Conclusions: A higher rate of normal sella morphology was detected on CBCT compared with studies conducted with wider age range.Oval sella was mostly observed with normal sella, and flattened sella was mostly determined with irregularity in posterior part of dorsum sella.CBCT is a useful technique to examine morphological types of sella turcica.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".