26 Improving timeliness of specialist referral and diagnosis for patients with suspected lung cancer through standardization
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
<h3>Background</h3> 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. <h3>Objectives</h3> Decrease time from suspicious CT chest to LC clinic referral and decrease time from referral to diagnosis and staging. <h3>Methods</h3> 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. <h3>Results</h3> 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). <h3>Conclusions</h3> Standardization of radiologist reporting and LC clinic triage led to significant improvement in timeliness of specialist access, diagnosis and staging investigations.
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 | 0.000 | 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