Role of Computed Tomographic Cisternography in the Management of Cerebrospinal Fluid Rhinorrhea
Bibliographic record
Abstract
In a retrospective review of 13 patients, computed tomographic cisternography (CTC) was the primary imaging modality used for the detection of cerebrospinal fluid (CSF) leaks. In five of the cases, the diagnosis of CSF rhinorrhea was confirmed by the beta2-transferrin test. In the remaining cases, it was corroborated through endoscopic visualization, clinical history, and nuclear scanning. This study analyzes the efficacy of CTC in the detection of CSF leaks and discusses the different methods of computerized manipulation and reconstruction of the images for effective site localization. The study demonstrates that computerized reconstruction of images should be considered an integral part of CTC because it appears to be an inexpensive and simple diagnostic tool that improves on the accuracy of detection. Although T2-weighted magnetic resonance imaging may be helpful, this study emphasizes the efficacy of CTC in the diagnosis of CSF leaks. Using the techniques of image reconstruction improves on diagnostic precision with relatively little increase in cost, time, and labour. This study also introduces a diagnostic algorithm for otolaryngologists dealing with the challenge of identifying and locating CSF leaks.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".