The use of PET scans in the management of non-small cell lung cancer patients relative to evidence-based indications across Canada.
Bibliographic record
Abstract
104 Background: PET scans are playing an increasingly important role in the management of cancer patients, particularly those diagnosed with non-small cell lung cancer (NSCLC). At the same time, the high capital and operating costs of the technology in the publicly funded Canadian health care system presents a strong case for monitoring use of PET scans relative to evidence based indications. This study by the Canadian Partnership Against Cancer reports on the use of PET scans in the management on NSCLC across Canada. Methods: PET scan utilization data from all hospitals providing the service in six Canadian provinces was linked to provincial cancer registry data, which allowed for the identification of demographic, diagnostic/prognostic, and treatment information on each NSCLC patient receiving a PET scan. The indication for each scan was then inferred from the timing of the scan relative to the dates of diagnosis, neoadjuvant therapy, surgery, curative chemo-radiation, or palliative treatment, as relevant for each patient’s care pathway. This way, the scan indications were identified as one of the following: staging, treatment planning, assessing treatment response during active treatment, and assessing metastases following end of curative treatment or palliative treatment. Results: The study results were not yet available at the time of submission of this abstract. Preliminary results and analyses will be available by the November 1, 2013, conference date. The results will include the following indicators for the 2009-2011 timeframe: percentage of NSCLC cases receiving a PET scan by stage; percentage NSCLC PET scans that are performed for each of the identified indications (diagnosis/staging, treatment planning, treatment response, follow up) by stage at diagnosis; variations by province, age group, and sex. Conclusions: This work will identify patterns in the use of PET scans for NSCLC patients across Canada and assess the extent to which this use is consistent with currently supported evidence based indications. The results will help inform more evidence-based use of the diagnostic technology across Canada and will form a baseline for future monitoring as the 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 | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".