Non-Medical Costs of Colorectal Cancer Screening Using Computed Tomographic Colonography
Bibliographic record
Abstract
Purpose: To estimate the non-medical costs of colorectal cancer (CRC) screening using computed tomographic colonography (CTC). Methods: Consecutive individuals presenting for CRC screening at a Calgary community diagnostic imaging centre were recruited. Subjects completed a questionnaire including items on time-off work both for the subject and any accompanying caregiver, travel details and direct out-of-pocket expenses (bowel prep). Time costs were valued at Government of Canada wage rates. Travel costs included estimated costs for travel by car and actual parking costs and taxi and public transportation fares. Car user's costs were calculated using a Canadian Automobile Association estimate of motoring costs per kilometre. Costs are in 2007 Canadian dollars. Results: 132 of 325 subjects undergoing CRC screening with CTC between November 2007 and May 2008 consented to receive a questionnaire in the mail. Eighty subjects returned the questionnaire for an overall response rate of 25%. The mean age of the sample was 57, 66% were male and 64% were employed. Thirty-four percent of the subjects required an accompanying caregiver. The non-medical costs (subject ± caregiver) averaged $154. The breakdown of subject ± caregiver time and travel costs is found in the Table.Table: Non-medical time and costs of colorectal cancer screeningConclusion: Conclusion: The non-medical costs of CRC screening with CTC are significant, but less than they are for colonoscopy ($308). These costs are important given that they may impact a person's ability to comply with CRC screening. Furthermore, recent guidelines recommend their inclusion in economic evaluations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".