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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".