A rural CT scanner: evaluating the effect on local health care.
Bibliographic record
Abstract
OBJECTIVE: The first small rural hospital in Ontario to propose a computed tomography (CT) scanner was in Walkerton, a town 160 km north of London. The Ontario Ministry of Health approved the proposal as a pilot project to evaluate the effect on local health care of a rural scanner. This evaluation study had 3 parts: a survey of physicians, a survey of patients, and an analysis of population CT scanning rates. METHOD: The physicians in the area served by the scanner were asked about its impact on their care of their patients in a mailed questionnaire and in semistructured interviews. Scanner outpatients were given a questionnaire in which they rated the importance of its advantages. The analysis of scanning rates--the ratio of number of scans to estimated population--compared rates in the area with other Ontario rates before and after the scanner was introduced. RESULTS: The physicians reported that local CT allowed them to diagnose and treat patients sooner, closer to home, and with greater confidence. On average, 75% of the patients ranked faster and closer access as very important. Scanning rates in the area rose, although they did not match urban rates. CONCLUSIONS: The study confirms that the rural scanner changed the area's health care in significant ways and that it helped to narrow the gap between rural and urban service levels. We recommend that CT be expanded to other rural regions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".