Presence and types of anterior clinoid process pneumatization, evaluated by Multidetector Computerized Tomography
Bibliographic record
Abstract
PURPOSE: The types and ratio of anterior clinoid process (ACP) pneumatization in paranasal sinus Multidetector Computerized Tomography (MDCT) were investigated the importance of ACP in neurosurgical approaches discussed. METHODS: Paranasal MDCT images of 499 subjects (259 male, 240 female), between 17 and 65 years of age, were included in the study. Presence and types of pneumatization of the ACP and pneumatization types (I, II or III) were evaluated. RESULTS: ACP pneumatization was detected in 37.5% of the males and 33.3% of the females. Right, left and bilateral ACP pneumatizations were detected in 12.7%, 9.3% and 15.4% of males and 9.2%, 8.3% and 15.8% of the females, respectively. The most commonly detected types of pneumatization were Type I (49.1%) for right pneumatizations and Type II pneumatization for left (40.9%) and bilateral (37.2%) pneumatizations. In males, Type I (37.1%) and in females, Type II (40.0%) pneumatizations were detected more frequently. Type III pneumatization was detected in 29.9% of the males and 22.5% of the females. ACP pneumatization ratios were higher in younger subjects and lower in older subjects. CONCLUSION: Sclerosis process related to aging may be responsible for the lower pneumatization ratios in older subjects. When Type III ACP pneumatization is present, clinoidectomy should not be performed: in this type of ACP pneumatization, cerebrospinal fluid fistula develops in all cases.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".