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Record W7117321436 · doi:10.1080/07357907.2025.2599381

Diagnostic Costs of Metastatic and Unknown Primary Cancers in Alberta, Canada, 2017–2021

2025· article· en· W7117321436 on OpenAlexaffabout
Arianna Waye, Nguyễn Xuân Thành, Allison N. Scott, Tara R. Bond, Douglas A. Stewart

Bibliographic record

VenueCancer Investigation · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Diagnosis and Treatment
Canadian institutionsAlberta HealthAlberta Health Services
Fundersnot available
KeywordsStage (stratigraphy)CancerDiagnostic testCost analysisDiseaseCost driverHealth careBaseline (sea)Total cost

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.276
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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