Expected Cost Savings From Low-Dose Computed Tomography Scan Screening for Lung Cancer in Alberta, Canada
Bibliographic record
Abstract
Introduction: The expensive modern therapeutic regimens for advanced lung cancer (LC) stages have been recently approved. We evaluated whether low-dose computed tomography (LDCT) LC screening of high-risk Albertans is cost saving. Methods: We used a decision analytical modeling technique with a health system perspective and a time horizon of 3 years to compare benefits associated with reduced health service utilization (HSU) from earlier diagnosis to the costs of screening. Using patient-level data, HSU costs by stage of disease were estimated for patients with LC, including inpatient, outpatient, and physician services, and costs for prescription drugs and cancer treatments. Results: Of 101,000 people aged 55 to 74 years eligible for screening, an estimated 88,476 scans would be performed in Alberta in 3 years. Given LDCT sensitivity and specificity of 90.5% and 93.1%, respectively, we estimated that a stage shift toward earlier diagnosis would be expected whereby 43% more patients would be identified at stage 1 or 2 as compared with without screening. The estimated cost of screening is $35.6 million (M), whereas the stage shift associated with screening would avoid $42M in HSU costs. The net cost avoidance associated with screening is therefore $6.65M. The probability for the screening to be cost saving is estimated at 72%. Conclusions: This study has revealed that LDCT LC screening is likely to be cost saving in Alberta. Adoption of this program into the provincial health care system is worth considering provided constraints in the system related to surgical capacity and CT wait times could be addressed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".