Diagnostic Costs of Metastatic and Unknown Primary Cancers in Alberta, Canada, 2017–2021
Bibliographic record
Abstract
Given the aggressive symptomatic nature of Cancer of Unknown Primary (CUP) and non-standardized diagnostic approaches, it is expected that the cost of CUP diagnosis is higher than other cancers. This study aims to investigate the diagnostic costs of CUP as compared to other types of metastatic cancer and non-metastatic cancers over a 5-year period in the provincial health authority of Alberta, Canada. Health service utilization (inpatient, outpatient, physician services) and associated costs were compared in the 6-months prior to the diagnostic date, to the baseline period for CUP and non-CUP patients. Costs were estimated using claims amounts paid to physicians and by multiplying the resource intensity weight with the cost per standard hospital stay in Alberta. Diagnostics for CUP cost $9005 per-patient, which were 1.8x more costly than other metastatic cancers ($5027). These higher costs were driven by a large proportion of patients being diagnosed in hospital. Our findings revealed that the per-patient cost of a CUP hospital diagnosis ($13,925) was 10x that of a CUP community diagnosis ($1225) and community diagnosis of a stage IV cancer ($1426). Potential benefits exist in streamlining efforts to diagnose early, improve patient health outcomes, and reduce overall system costs of diagnosing CUP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".