Examining the use of PET scans in the diagnosis and management of non-small cell lung cancer patients.
Bibliographic record
Abstract
311 Background: PET Scans are increasingly used in the diagnosis and management of non-small cell lung cancer (NSCLC) patients. However, uptake of PET at provincial level is not well studied. This project, led by the Canadian Partnership Against Cancer, established processes and indicators to describe utilization of PET in patients with NSCLC. These indicators support the monitoring of uptake and highlight areas for quality improvement strategies at the national and provincial level. Methods: Cases of NSCLC, diagnosed in the study period of 2009-2011, were identified from cancer registries and linked to PET utilization data. PET scans were identified as indicated for diagnosis/staging or treatment response, based on the timing of scans relative to diagnosis and treatment dates. Scans conducted three months prior to and up to four months post-diagnosis but before start of treatment (surgery or radiation) were identified as diagnosis/staging. Scans conducted after the start of treatment to ten weeks post-treatment were identified as management and follow-up of treatment. Results: A total of 27,984 cases of NSCLC were identified. Preliminary analysis revealed that 8,947 (32.0%) of NSCLC patients had at least one PET scan. Some variation was seen in age, with those 18 to 69 years more likely to receive a scan than those 70 years and older. PET scan use was higher among stages I and II (52.3% to 50.6%) compared to stage IV (17.98%). A majority of PET scans were performed for diagnosing/staging NSCLC (91.1%). PET scans for diagnosis/staging were highest for patients with stage I (36.7%) followed by stage IV (24.6%). Conclusions: This study provided information on the current use of PET technology across Canada, allowing for identification of opportunities for increasing evidence-based use while decreasing extra-evidential use, and forming a baseline for future monitoring as evidence evolves.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".