Predicting Positive CT Findings in Non-Polypoid Para-Nasal Sinus Disease
Bibliographic record
Abstract
Objective: Chronic rhinosinusitis affects significant portion of the population and is one of the most common reasons for Otolaryngology visits. In recent years, Computed Tomography (CT) has become the main modality of investigation in sinus disease. However, significant paucity in evidence exists in correlating symptoms and laboratory findings with positive CT findings. Given the associated radiation exposure and significant cost on the healthcare system, clinical guidelines in determining the appropriateness of CT investigation are needed. Study Design: Eighty-three consecutive patients referred to a single rhinologist for evaluation for chronic rhinosinusitis without nasal polyposis were retrospectively reviewed. Setting: The study involved two McGill University associated hospitals in a 13 month period. Subjects and Methods: Patients were evaluated for presence of six subjective symptoms and two objective signs. Complete blood count and CT sinuses were then ordered. CT sinuses were subsequently graded using the Lund-McKay scoring system. Results: No single symptom, sign or combinations were predictive of positive CT finding. No laboratory markers were able to predict a positive scan. Interestingly inflammatory markers were lower in positive scan group compared to the negative scan population. Conclusion: Clinical diagnosis of chronic rhinosinusitis in nonpolypoid patients presents a challenge. CT imaging is a fundamental component in diagnosing those patients, yet clinical experience is important in preventing unnecessary radiation exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".