Cost and Resource Utilization in Cervical Cancer Management: A Real-World Retrospective Cost Analysis
Bibliographic record
Abstract
OBJECTIVES: We set out to assess the health care resource utilization and cost of cervical cancer from the perspective of a single-payer health care system. METHODS: Retrospective observational data for women diagnosed with cervical cancer in British Columbia between 2004 and 2009 were analyzed to calculate patient-level resource utilization patterns from diagnosis to death or 5-year discharge. Domains of resource use within the scope of this cost analysis were chemotherapy, radiotherapy, and brachytherapy administered by the BC Cancer Agency; resource utilization related to hospitalization and outpatient visits as recorded by the B.C. Ministry of Health; medically required services billed under the B.C. Medical Services Plan; and prescriptions dispensed under British Columbia's health insurance programs. Unit costs were applied to radiotherapy and brachytherapy, producing per-patient costs. RESULTS: The mean cost per case of treating cervical cancer in British Columbia was $19,153 (standard error: $3,484). Inpatient hospitalizations, at 35%, represented the largest proportion of the total cost (95% confidence interval: 32.9% to 36.9%). Costs were compared for subgroups of the total cohort. CONCLUSIONS: As health care systems change the way they manage, screen for, and prevent cervical cancer, cost-effectiveness evaluations of the overall approach will require up-to-date data for resource utilization and costs. We provide information suitable for such a purpose and also identify factors that influence costs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".