Navigating the Complexity of Cancer Diagnosis: \nSynthesis Maps for Diagnosis of Common Cancers in Canadian Contexts
Bibliographic record
Abstract
Two synthesis maps represent clinical cancer processes and the patient experiences associated with the processes. The maps were constructed in an iterative design and research process by the OCAD University Health Design Studio for CPAC, as part of a larger collaboration to identify evidence-based opportunities for system-level change in cancer diagnosis to improve patient experience and clinical processes across Canada. The maps present an integration of current knowledge from clinical practice and patient experiences drawn directly from interviews and workshops with patient advisors and primary care/cancer clinicians. \nThe Clinical Process Map of Canadian Cancer Pre-Diagnosis (Clinical) map represents the complex steps of diagnosis typical of patients living within three geographic regions, for three cancer sites that represent a much wider variety of cancers than can be depicted here. The Patient Experience in Cancer Pre-Diagnosis (Patient) map presents three distinct pathways for the same cancers. The Patient map is presented as a metaphorical maze, consistent with the psychosocial experience of patients struggling with these cancers, as informed by patient advisors in interviews and workshops. \nThe primary purpose of both maps is to demonstrate the relative real-world complexity of cancer diagnosis processes as understood by the stakeholders of diagnosis, patients and clinicians. The maps reveal the variability of access to care across geographies, indicating habitation as one of the significant social determinants of health outcome. While there are other determinants, key leading proposals for system intervention are responsive to problematic location. The various recommended interventions (e.g. promising solutions to some of these systemic problems) are developed from emerging models of diagnostic practice for improving cancer outcomes in Canadian provinces and territories, as well as established best practices currently used in other countries than Canada. \nBoth maps show the stage of the cancer lifecycle referred to as suspicion to diagnosis. This diagnostic interval is recognized as the period between a patient’s presentation of concerns to receiving a confirmed diagnosis from a cancer specialist, up to the point of entering subsequent treatment for that diagnosis. Both maps clearly indicate the point of transition to cancer treatment for the pathways.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".