Implementing A One-Day Testing Model Improves Timeliness of Workup for Patients with Lung Cancer
Bibliographic record
Abstract
Background: Patients with lung cancer often experience stressful delays throughout the diagnostic phase of care. To address that situation, our multidisciplinary team created a “Navigation Day,” during which patients partake in a single-day visit that comprises nurse-led teaching, social work, smoking cessation counselling, symptom control, and dedicated test slots for integrated positron-emission tomography and computed tomography (PET/CT), pulmonary function tests (PFTS), and magnetic resonance imaging (MRI) of the brain. We evaluated the effects of that program on wait times and patient satisfaction. Methods: Patients with a suspicion of lung cancer on chest ct imaging referred during 3 time periods were reviewed: 1 year before launch of the Navigation Day, 1 year post-launch, and 2 years post-launch. Patients were further stratified according to concordance of their test date with a Navigation Day date. Mean wait times for PET/CT, PFTS, and MRI brain were calculated for each group. Patient satisfaction was measured using a standardized provincial survey. The Student t-test and analysis of variance were used to assess for significance. Results: After implementation, mean wait times in the first year improved to 9.2 days from 15.5 days for PET/CT (p < 0.0001), to 9.6 days from 15.7 days for PFTS (p < 0.0001), and to 10.2 days from 16.0 days for MRI brain (p < 0.0001). Patients who used a dedicated test slot experienced the shortest wait times, at 5.8 days for PET/CT, 5.8 days for pfts, and 6.3 days for mri brain (p < 0.0001). Those improvements were sustained at 2 years post-launch. Conclusions: Patient satisfaction in the categories of assistance, emotional support, and clarity remained high post-launch. Navigation Day significantly improved the timeliness of diagnostic testing services in patients with suspected 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".