26 Improving timeliness of specialist referral and diagnosis for patients with suspected lung cancer through standardization
Bibliographic record
Abstract
Background Delays in lung cancer (LC) diagnosis are associated with worse clinical outcomes. Our rapid assessment LC clinic identified referral delays following thoracic imaging suspicious for LC and delays associated with unstructured triage. Objectives Decrease time from suspicious CT chest to LC clinic referral and decrease time from referral to diagnosis and staging. Methods Retrospective baseline chart review (Jan–Apr 2018) and prospective monitoring (May 2018–May 2019). PDSA cycles: 1) Standardized Triage Pathways (nurse-physician triage to diagnostic pathways, pre-ordered staging tests, small nodule clinic); 2) local standardization and regional implementation of CT reporting recommending LC clinic referral (March 2019). Data include dates of: imaging suspicious for LC, CT chest, specialist referral and assessment, staging tests, radiologist recommendations and diagnosis. Data are reported as mean days; statistical process control XbarS charts and unpaired t-tests were used to assess for significance. Results Following PDSA 1, there were reductions in mean time from referral to PET (40.5 to 27.3 days), to CT/MRI Brain (35.8 to 18.8 days), and to diagnosis (41.4 to 30.1 days), all significant by special cause variation. Following PDSA 2, the percentage of LC clinic patients with a CT chest recommending clinic referral increased (25.2% to 37.0%, p=0.041), with increased recommendations from regional hospitals (4.2% to 16.5%, p=0.022). When a radiologist recommended LC clinic referral, time to referral and assessment were faster (7.3 vs. 15.5 days, p=0.0001; 20.3 vs. 26.2 days, p=0.001, respectively). Conclusions Standardization of radiologist reporting and LC clinic triage led to significant improvement in timeliness of specialist access, diagnosis and staging investigations.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".