The Value of Thoracic Computed Tomography Scans in Clinical Diagnosis: A Prospective Study
Bibliographic record
Abstract
BACKGROUND: Computed tomography (CT) scans are used extensively to investigate chest disease because of their cross-sectional perspective and superior contrast resolution compared with chest radiographs. These advantages lead to a more accurate imaging assessment of thoracic disease. The actual use and evaluation of the clinical impact of thoracic CT has not been assessed since scanners became widely available. OBJECTIVE: To identify patterns of utilization, waiting times and the impact of CT scan results on clinical diagnoses. DESIGN: A before and after survey of physicians who had ordered thoracic CT scans. SETTING: Vancouver General Hospital--a tertiary care teaching centre in Vancouver, British Columbia. SUBJECTS: Physicians who had ordered CT scans. INTERVENTION: Physicians completed a standard questionnaire before and after the CT scan result was available. MEASUREMENTS: Changes in the clinical diagnosis, estimates of the probabilities for the diagnosis both before and after the CT scan, and waiting times. RESULTS: Four hundred fifty-four thoracic CT cases had completed questionnaires, of whom 80% were outpatients. A change in diagnosis was made in 48% of cases (25% with a normal CT scan and 23% with CT scan findings that indicated a different diagnosis). The largest change in probability scores for the clinical diagnosis before and after the CT scan was 43.9% for normal scans, while it was 36.3% for a different diagnosis and 26.3% for the same diagnosis. High-priority scans were associated with decreased waiting time (--7.89 days for each unit increase in priority). CONCLUSIONS: The CT scan results were associated with a change in diagnosis in 48% of cases. Normal scans constituted 25% of the total and had the greatest impact scores. Waiting times were highly correlated with increased urgency of the presenting problem.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".