Faster and better: Improving the diagnostic phase of lung cancer at a system level.
Bibliographic record
Abstract
115 Background: For many patients going through diagnostic testing for cancer, the time from suspicion to diagnosis or rule-out, can be a confusing and anxious time. In 2007, Cancer Care Ontario began investing in the implementation of diagnostic assessment programs (DAPs) across Ontario, Canada to improve the quality of care during the diagnostic phase of lung cancer. DAPs consist of multidisciplinary healthcare teams that manage and coordinate a patient’s diagnostic care from testing to a definitive diagnosis. The objectives of the DAPs are to: 1) decrease time from suspicion to diagnosis or resolution; 2) optimize the patient’s experience during the diagnostic process; 3) optimize satisfaction and experience among primary care providers and specialists; and 4) provide a sustainable solution by offering good value for money. Today over 35,000 patients have been diagnosed in one of the 18 lung DAPs that exist across the province. Methods: The implementation of DAPs featured the introduction of a patient navigator to act as the primary point of contact for patients, improve the patient experience and ensure their patients were progressing through any required diagnostic imaging and consultations in a timely manner. Cancer Care Ontario also engaged with primary care providers to refer patients with findings suspicious for lung cancer to DAPs as early as possible to ensure they benefited from organized assessment. Cancer Care Ontario has collected patient level data to measure wait times and implemented a patient survey to assess patient experience. Results: In the past five years, the median wait time from referral to a lung DAP to diagnosis or rule out has decreased by 19% to 24 days and the 90th %tile has decreased by 28% to 51 days. The large majority of patients have had a positive experience with their DAPs, with 95% of patients scoring their experience in the diagnostic process as “good” or “excellent”. Conclusions: The implementation of DAPs across the province is seen as a valuable component of quality of care by improving the diagnostic phase of cancer. The sustainability of the DAP model is demonstrated by the continued improvements in access and maintained patient experience in spite of growing volumes (91% increase in the past five years).
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".