Anatomical Variations on Routine CT Scans Observed in the Paranasal Sinuse
Bibliographic record
Abstract
Objective: The aim of current study is to determine the frequency of anatomical variants on routine CT scan observed in the paranasal sinus. Stud Design: Cross-sectional Place and Duration: The study was conducted at Radiology department of Jinnah Hospital, Lahore for the duration of nine months from January 2021 to September 2021. Methods: There were 90 sinus patients of both genders with ages 20-55 years in this study. Cases were recruited after informed written permission was obtained and data such as gender and BMI were collected. The prevalence of various anatomical variations of the sinonasal cavities was determined based on the results of the CT scans. Anatomical differences between individuals with low to no apparent imaging evidence of rhinosinusitis vs those with clinically substantial radiologic evidence of rhinosinusitis were examined in this study. SPSS 21.0 was used to analyze complete data. Results: Among 90 cases, the majority of the patients were males 63 (70%) and 27 (30%) were females with mean age 33.41±8.56 years and mean BMI 23.22 ±5.31 kg/m2. According to Kero’s classification to determine the difference in olfactory fossa depth, most of the patients 62 (68.9%) were in type II, 20 (22.2%) in type I and 8 (8.9%) in type III. In accordance with anatomical variation, we found that most of the patients 65 (72.2%) had deviated nasal septum (DNS) followed by agger nassi cells in 61 (67.8%) cases and concha bullosa in 35 (38.9%) cases. Conclusion: We concluded in this study that the prevalence of anatomical variants among patients of paranasl sinus was higher in which the majority of the patients had deviated nasal septum and agger nassi cells. Keywords: Frontal sinus, Paranasal Sinus, Maxillary sinus, Anatomical variants
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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 | 0.002 |
| 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 | 0.000 | 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".