Managing Incidentalomas Safely: Do Computed Tomography Requisitions Tell Us What We Need to Know?
Bibliographic record
Abstract
PURPOSE: Technological advancements and the ever-increasing use of computed tomography (CT) have greatly increased the detection of incidental findings, including tiny pulmonary nodules. The management of many "incidentalomas" is significantly influenced by a patient's history of cancer. The study aim is to determine if CT requisitions include prior history of malignancy. METHODS: Requisitions for chest CTs performed at our adult tertiary care hospital during April 2012 were compared to a cancer history questionnaire, administered to patients at the time of CT scan. Patients were excluded from the study if the patient questionnaire was incomplete or if the purpose of the CT was for cancer staging or cancer follow-up. RESULTS: A total of 569 CTs of the chest were performed. Of the 327 patients that met inclusion criteria, 79 reported a history of cancer. After excluding patients for whom a history of malignancy could not be confirmed through a chart review and excluding nonmelanoma skin cancer, dysplasia, and in situ neoplasm, 68 patients were identified as having a history of malignancy. We found 44% (95% confidence interval [0.32-0.57]) of the chest CT requisitions for these 68 patients did not include the patient's history of cancer. Of the malignancies that were identified by patient questionnaire but omitted from the clinical history provided on the requisitions, 47% were malignancies that commonly metastasize to the lung. CONCLUSIONS: A significant number of requisitions failed to disclose a history of cancer. Without knowledge of prior malignancy, radiologists cannot comply with current guidelines regarding the reporting and management of incidental findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".