Expediting specialist referral for patients with suspected lung cancer through standardization of radiologist reporting
Bibliographic record
Abstract
RATIONALE Lung cancer (LC) diagnostic pathways are often initiated following suspicious clinical and radiologic findings. We identified delays from first thoracic imaging suspicious for LC to specialist evaluation in a rapid assessment clinic [Lung Diagnostic Assessment Program (LDAP)].OBJECTIVES We evaluate the impact of a quality improvement initiative consisting of standardized Computed Tomography (CT) reporting on timeliness of LC diagnostic pathways.METHODS Retrospective chart review of LDAP-referred patients established baseline data (January – December 2018). Improvement initiatives included (i) implementation of standardized LDAP referral recommendations at an academic center for patients with suspected LC by thoracic imaging (January 2019), and (ii) spread of standardized reporting to 3 community hospitals (March 2019). Prospective chart review (January - September 2019) evaluated for improvement. Data include dates of CT chest, LDAP referral/assessment and specific phrasing of radiology reports. Continuous data are reported as medians, categorical data as percentages; Mann-Whitney U and chi-squared tests assess for significance.MEASUREMENTS AND MAIN RESULTS We reviewed 1,244 LDAP referrals (697 baseline; 547 post-standardization). Patients with a radiologist recommendation for LDAP referral had faster times from CT to referral (median [75th, 90th percentile]) (5[9,15] vs. 6[16,33] days) and specialist assessment (20[27,35] vs. 22[33,50] days). Following standardization, the percentage of LDAP-referred patients with a radiologist recommendation for referral increased (29.2% to 48.3%; P < .001), significant for the academic center (50.2% to 61.8%; P = .006) and community hospitals (12.1% to 35.3%; P < .001).CONCLUSIONS Standardized radiologist reporting and specialist referral recommendations for patients with imaging suspicious for LC leads to faster patient referral and assessment.
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How this classification was reachedexpand
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".