Improving Timeliness of Lung Cancer Diagnosis and Staging Investigations Through Implementation of Standardized Triage Pathways
Bibliographic record
Abstract
PURPOSE: Timely care for patients with lung cancer (LC) is associated with improved clinical outcomes. In Southeastern Ontario, Canada, we identified delays in the diagnostic process for patients undergoing evaluation for suspected LC through a rapid assessment clinic. We developed improvement initiatives with an aim of reducing the time from referral to diagnosis. METHODS: A Standardized Triage Process (STP) was implemented for patients referred with suspected LC, including routine interdisciplinary triage, standardized pathways with preordered staging tests, and a new Small Nodule Clinic. We retrospectively analyzed all patients referred pre-STP (January to April 2018) and prospectively for improvement (May 2018 to March 2019). Process measures included STP compliance and time to completion of staging investigations (positron emission tomography [PET] and computed tomography/magnetic resonance imaging of brain). Data are reported as means; significance was determined by special-cause variation using Statistical Process Control charts; unpaired t tests were compared between groups. RESULTS: We reviewed 833 referrals (207 baseline and 626 post-STP). STP compliance improved monthly to 99.4%. Post-STP, time from referral to PET decreased (from 38.5 to 15.7 days), time from referral to brain imaging decreased (from 33.4 to 13.1 days), and time from referral to diagnosis decreased (from 38.0 to 22.7 days), all demonstrating special-cause variation. Patients completing preordered staging tests experienced significantly faster care than those without preordered tests, including time to PET (23.0 v 35.9 days), computed tomography/magnetic resonance imaging of brain (16.2 v 29.9 days), and diagnosis (39.9 v 28.1 days), all P < .001. CONCLUSION: An STP significantly improved timeliness of diagnosis and staging for patients with suspected LC undergoing evaluation in a rapid assessment clinic.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".