Follow‐Up of Incidental High‐Risk Pulmonary Nodules on Computed Tomography Pulmonary Angiography at Care Transitions
Bibliographic record
Abstract
BACKGROUND: Computed tomography pulmonary angiography (CTPA) detects incidental findings that require follow-up. In just over 50% of cases, those incidental findings are pulmonary nodules. Fleischner guidelines recommend that patients with nodules that have a high risk of malignancy should undergo CT follow-up within 3-12 months. OBJECTIVE: We examined the proportion of patients with pulmonary nodules requiring follow up who received repeat imaging within six weeks of the time frame recommended by the radiologist. DESIGN: This retrospective cohort study included all patients who underwent CTPA in the emergency department and inpatient settings at three teaching hospitals in Toronto, Canada between September 1, 2014, and August 31, 2015. Natural language processing software was applied to a linked radiology information system to identify all CTPAs that contained pulmonary nodules. Using manual review and prespecified exclusion criteria, we generated a cohort with possible new lung malignancy eligible for follow-up imaging; then we reviewed available health records to determine whether follow-up had occurred. RESULTS: Of the 1,910 CTPAs performed over the study period, 674 (35.3%) contained pulmonary nodules. Of the 259 patients with new nodules eligible for follow-up imaging, 65 received an explicit suggestion for follow-up by radiology (25.1%). Of these 65 patients, 35 (53.8%) did not receive repeat imaging within the recommended time frame. Explicit mention that follow-up was required in the discharge summary (P = .03), attending an outpatient follow-up visit (P < .001), and younger age (P = .03) were associated with receiving timely follow-up imaging. CONCLUSIONS: Over 50% of patients with new high-risk pulmonary nodules detected incidentally on CTPA did not receive timely follow-up imaging.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".