Establishing Diagnostic Reference Levels for CT Through a Provincial Medical Informatics Metadata Repository in Ontario
Bibliographic record
Abstract
PURPOSE: To determine whether computed tomography radiation dose data could be captured electronically across hospitals to derive regional diagnostic reference levels for quality improvement. METHODS: Data on consecutive computed tomography examinations from 8 hospitals were collected automatically in a central database (Repository) from April 2017 to September 2017. The most frequently performed examinations were used to determine the standard protocols for each hospital. Diagnostic reference levels across hospitals were derived using statistical distribution for 2 radiation dose metrics. These values were compared between hospitals, within and between hospitals by scanner and against national Health Canada achievable doses and diagnostic reference levels. RESULTS: Three master protocol groups, Head, Abdomen-Pelvis, and Chest-Abdomen-Pelvis, accounted for 43% of all valid studies (N = 40 277). For the Repository, 11 of 12 mean values and 75th percentile diagnostic reference levels were below the Health Canada mean and 75th percentile values, and one was the same as the Health Canada value. Mean radiation dose by protocol varied by as much as 97% between hospitals. There was no consistent pattern in the difference between mean doses between large and small hospitals. CONCLUSION: This electronic data acquisition process could be used to continually update achievable doses for frequently used computed tomography examinations in Ontario and eliminate the need for nationwide manual surveys. Results compared across institutions will allow hospitals to maintain achievable doses and lower patient exposure.
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 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 | 0.012 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".