Wait Times for Diagnosis and Treatment of Lung Cancer: A Single-Centre Experience
Bibliographic record
Abstract
Background: Multiple clinical practice guidelines recommend rapid evaluation of patients with suspected lung cancer. It is uncertain whether delays in diagnosis and management have a negative effect on outcomes. Methods: This retrospective study included 551 patients diagnosed with lung cancer through the diagnostic assessment program at the Institut universitaire de cardiologie et de pneumologie de Québec between September 2013 and March 2015. Median wait times between initial referral, diagnosis, and first treatment were calculated and compared with recommended targets. Analyses were performed to evaluate for specific factors associated with longer wait times and for the effect of delays on the outcomes of progression-free survival (PFS), relapse-free survival (RFS) after primary surgical resection, and overall survival (OS). Results: Most patients were investigated and treated within recommended targets. Of the entire cohort, 379 patients were treated at our institution. Of those 379 patients, 311 (82%) were treated within recommended targets. In comparing patients within and outside target times, the only statistically significant difference was found in the distribution of treatment modalities: patients meeting targets were more likely to be treated with surgery or chemotherapy rather than with radiation. The PFS on first treatment modality was influenced by clinical stage, but not by time to therapy [hazard ratio (HR): 1.10; p = 0.65]. The OS for the entire cohort was also influenced by stage, but not by delays (HR: 1.04; p = 0.87). For the 209 patients treated by surgery with curative intent, a significant reduction in RFS was associated with male sex and TNM stage, but not with delays (HR: 1.11; p = 0.83). The OS after primary surgical resection was also associated with TNM stage, but not with delays (HR: 1.82; p = 0.43). Conclusions: Recommended targets for wait times in the investigation and treatment of lung cancer can be achieved within a diagnostic assessment program. Compared with radiation treatment, treatment with surgery or chemotherapy is more likely to be completed within targets. Delays in investigation and treatment do not appear to negatively affect the clinical outcomes of OS, RFS, and PFS. Prospective studies are needed to evaluate whether efficient work-up and treatment influence other important variables, such as quality of life, cost of care, and access to therapies while performance status is adequate.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".