A comparison of film-screen, CR and DR: a community hospital time-motion study.
Bibliographic record
Abstract
The purpose of this study was to compare technologist efficiency for conventional radiography, computed radiography (CR) and direct radiography (DR) for two types of general x-ray examinations. The study was performed at St. Joseph's Health Centre, in Toronto, Canada. The study spanned eight calendar months. Two views of the chest and three views of the ankle were chosen as representative examinations for analysis. Patient examination times were recorded on the radiology information system for both types of studies for conventional radiography, CR and DR. There was a significant difference in average time of examination for all three types of imaging formats for chest studies and between conventional radiography and CR or DR for ankle radiographs. There was no significant difference between examination times for ankle studies when CR and DR were compared. The median time of examination of the chest was 18 minutes, eight minutes and six minutes for conventional radiography, CR and DR respectively. The median time of examination for ankle radiographs were 22 minutes, seven minutes and five minutes for conventional radiography, CR and DR respectively. Technologists efficiency is significantly improved with the implementation of a DR system and CR system when compared to conventional radiography. DR may not deliver significant improvements in efficiencies for certain types of examinations.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".