Radiologic reporting for paranasal sinus computed tomography: A multi‐institutional review of content and consistency
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To optimize clinical care, radiologic reporting should consistently include clinically pertinent information. The purpose of this study was to: 1) determine the current satisfaction of otolaryngologists with paranasal sinus computed tomography (CT) radiologic reporting and 2) evaluate the comprehensiveness of paranasal sinus CT radiologic reporting. STUDY DESIGN: Two parts: 1) A national survey of all practicing otolaryngologists in Canada and 2) a retrospective review of paranasal sinus CT scan radiologic reporting. METHODS: A national survey of all Canadian otolaryngologists was conducted in September 2011. Questions were focused on eliciting the current satisfaction with sinus CT radiologic reporting. At two major centers (Alberta Health Services-Calgary Zone and the Ottawa Hospital), all sinus CT scans performed over a 2-year period were identified (9,739), and 100 from each center were randomly selected for analysis. The radiology reports were scrutinized to determine if seven critical and 11 noncritical items were mentioned. RESULTS: Many (22%) otolaryngologists are dissatisfied with current sinus CT radiologic reporting, and the majority (67%) would like more clinically useful information. All predefined sinus CT items were inconsistently reported. Anterior ethmoid artery anatomy, ethmoid skull base integrity, and sphenoethmoidal cell were the most infrequently reported critical items. CONCLUSIONS: This study has demonstrated that important information is inconsistently reported for sinus CT, and most otolaryngologists would like to see more clinically relevant content in radiology reports. Optimizing the reporting of sinus CT scans will improve communication between the radiologist and other clinicians managing patients with sinonasal disease. LEVEL OF EVIDENCE: 2b.
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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 | 0.042 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".