An Audit of CT Chest Reports and Their Potential Impact on the Workup of Patients with Suspected Lung Cancer
Bibliographic record
Abstract
Background. Quality gaps exist in the diagnostic evaluation of lung cancer patients. The initial CT chest guides the workup of patients with suspected lung cancer. We sought to determine how frequently CT reports provided guideline-concordant recommendations with regard to additional imaging studies and/or invasive diagnostic procedures. Methods. This was a retrospective study. The records of patients referred for investigation of suspected lung cancer between January 1, 2015, and June 30, 2016, were reviewed. Patients with confirmed lung cancer, for whom CT scan images and reports were available, are included. CT reports were reviewed, with attention to additional imaging studies and/or invasive diagnostic procedures suggested. These recommendations were examined against current guidelines for lung cancer diagnosis and staging, based on suspected disease stage. Results. One hundred forty-six patients are included in the analysis. Most patients were diagnosed with non-small-cell lung cancer (NSCLC), and 63% had advanced disease (stages III and IV). Only 12% of CT reports contained guideline-concordant recommendations for additional imaging studies, with PET scan suggested in only 6% of reports. Potential invasive diagnostic procedures were suggested in one fifth of CT reports, and only 58% of these recommendations were in keeping with current guidelines. In particular, transthoracic needle aspiration (TTNA) was suggested in 26% of patients despite advanced stage disease. Conclusion. Guideline-concordant recommendations for investigation of suspected lung cancer are rarely available on CT reports. This is true with respect to both imaging studies and invasive diagnostic procedures. Incorporation of more evidence-based suggestions may reduce quality gaps in lung cancer diagnosis and staging.
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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 | 0.015 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".