Reducing Wait Time for Lung Cancer Diagnosis and Treatment: Impact of a Multidisciplinary, Centralized Referral Program
Bibliographic record
Abstract
BACKGROUND: A multidisciplinary, centralized referral program was established at our institution in 2014 to reduce delays in lung cancer diagnosis and treatment following diagnostic imaging observed with the traditional, primary care provider-led referral process. The main objectives of this retrospective cohort study were to determine if referral to a Thoracic Triage Panel (TTP): 1) expedites lung cancer diagnosis and treatment initiation; and 2) leads to more appropriate specialist consultation. METHODS: Patients with a diagnosis of lung cancer and initial diagnostic imaging between March 1, 2015, and February 29, 2016, at a Memorial University-affiliated tertiary care centre in St John's, Newfoundland, were identified and grouped according to whether they were referred to the TTP or managed through a traditional referral process. Wait times (in days) from first abnormal imaging to biopsy and treatment initiation were recorded. Statistical analysis was performed using the Wilcoxon rank-sum test. RESULTS: A total of 133 patients who met inclusion criteria were identified. Seventy-nine patients were referred to the TTP and 54 were managed by traditional means. There was a statistically significant reduction in median wait times for patients referred to the TTP. Wait time from first abnormal imaging to biopsy decreased from 61.5 to 36.0 days (P < .0001). Wait time from first abnormal imaging to treatment initiation decreased from 118.0 to 80.0 days (P < .001). The percentage of specialist consultations that led to treatment was also greater for patients referred to the TTP. CONCLUSIONS: A collaborative, centralized intake and referral program helps to reduce wait time for diagnosis and treatment of lung cancer.
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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 | 0.000 |
| 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".