The Impact of Diagnostic Imaging Wait Times on the Prognosis of Lung Cancer
Bibliographic record
Abstract
OBJECTIVE: This study was performed to determine whether gaps in patient flow from initial lung imaging to computed tomography (CT) guided lung biopsy in patients with non-small cell lung cancer (NSCLC) was associated with a change in tumour size, stage, and thus prognosis. METHODS: All patients who had a CT-guided lung biopsy in 2009 (phase I) and in 2011 (phase II) with a pathologic diagnosis of primary lung cancer (NSCLC) at Eastern Health, Newfoundland, were identified. Dates of initial abnormal imaging, confirmatory CT (if performed), and CT-guided biopsy were recorded, along with tumour size and resulting T stage at each time point. In 2010, wait times for diagnostic imaging at Eastern Health were reduced. The stage and prognosis of NSCLC in 2009 was compared with 2011. RESULTS: In phase 1, there was a statistically significant increase in tumour size (mean difference, 0.67 cm; P < .0001) and stage (P < .0001) from initial image to biopsy. There was a moderate correlation between the time (in days) between the images and change in size (r = 0.33, P = .008) or stage (r = 0.26, P = .036). In phase II, the median wait time from initial imaging to confirmatory CT was reduced to 7.5 days (from 19 days). At this reduced wait time, there was no statistically significant increase in tumour size (mean difference, 0.02; P > .05) or stage (P > .05) from initial imaging to confirmatory CT. CONCLUSIONS: Delays in patient flow through diagnostic imaging resulted in an increase in tumour size and stage, with a negative impact on prognosis of NSCLC. This information contributed to the hiring of additional CT technologists and extended CT hours to decrease the wait time for diagnostic imaging. With reduced wait times, the prognosis of NSCLC was not adversely impacted as patients navigated through diagnostic imaging.
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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 | 0.001 | 0.003 |
| 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 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".