The Role of Multi Detector Computed Tomographic Imaging Prior to Clinic Consultation in Patients Suspected to Have Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Scanning patients early in their diagnostic journey helps in differentiating benign from malignant aetiology. There is increasing pressure on diagnostic practices for rapid diagnoses and thereby early commencement of treatment in patients suspected to have lung cancer (LC). In our practice, multi detector computed tomography (MDCT) imaging is performed in selected patients referred to the LC service prior to them seeing a chest physician in the LC clinic. This study evaluates the role of such practice and reviews its potential impact on LC services. METHODS: Prospective review of our practice from January 2007 to Apr 2007 was performed. Consecutive patients referred to the service with suspected LC were included. Chest radiograph (CXR) report and clinical information from general practitioners were reviewed and graded as high, medium or low risk for presence of LC. Patients with sufficient clinical and/or radiological concern underwent MDCT imaging prior to their clinic. Combined risk scores and modified risk scores were formulated and assessed against MDCT findings. RESULTS: A total of 139 patients were referred to the service, 124 of these had pre-clinic MDCT. Fifty-three patients (43%) had malignancy, 39 (31%) had non-malignant significant abnormalities, 17 (14%) had other incidental findings and 15 (12%) were normal. Modified combined risk score was the best predictor of presence of cancer. CONCLUSION: Pre-clinic MDCT scanning in patients with suspected LC is feasible and has a promising role in the modern care of LC patients. It also empowers physicians with additional information at the primary consultation.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".