Development and application of an electronic synoptic report for reporting and management of low-dose computed tomography lung cancer screening examination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Interpretation of Low Dose CT scans and protocol driven management of findings is a key aspect of lung cancer screening program performance. Reliable and reproducible methods are needed to communicate radiologists' interpretation to the screening program or clinicians driving management decision. METHODS: We performed an audit of a subset of dictated reports from the PANCAN study to assess for omissions. We developed an electronic synoptic reporting tool for radiologists embedded in a clinical documentation system software. The tool was then used for reporting as part of the Alberta Lung Cancer Screening Study and McGill University Health Centre Pilot Lung Cancer Screening Program. RESULTS: Fifty reports were audited for completeness. At least one omission was noted in 30 (70%) of reports, with a major omission (missing lobe, size, type of nodule in report or actionable incidental finding in recommendation section of report) in 24 (48%). Details of the reporting template and functionality such as automated nodule cancer risk assessment, Lung-RADS category assignment, auto-generated narrative type report as well as personalize participant results letter is provided. A description of the system's performance in its application in 2815 CT reports is then summarized. CONCLUSIONS: We found that narrative type radiologist reports for lung cancer screening CT examinations frequently lacked specific discrete data elements required for management. We demonstrate the successful implementation of a radiology synoptic reporting system for use in lung cancer screening, and the use of this information to drive program management and communications.
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.
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 | 0.001 | 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 | 0.000 |
| 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 it