Quality Initiative Program in its Sixth Year: Has it Become Part of our Radiology Culture?
Bibliographic record
Abstract
PURPOSE: The study sought to determine if the Quality Initiative Program (QUIP) has become part of the radiology culture at our institution. METHODS: After Research Ethics approval, QUIPs from January 2009 to December 2014 were assessed. We evaluated the response rates of radiologists receiving QUIPs to ensure they reviewed them. We performed a survey of radiologists and trainees to gain feedback regarding their perception of QUIPs in February 2014 and in June 2015. RESULTS: Response rates of radiologists receiving a QUIP improved, with 76% response rate in 2014 up from 66% in the first year and 42% in the second year. Based on the 2015 survey including radiologists and trainees, 75% agreed that QUIPs were educational, compared with 67% 16 months earlier. Fifty percent of respondents had changed their overall practice of reporting based on feedback from the QUIP in 2015 compared with 32% in 2014. In both surveys, 100% of respondents indicated that QUIPs have not been used against them for any disciplinary measure (or other negatively perceived action). When asked if there was a perceived decrease in stigma felt when a QUIP was received, 71% agreed or were neutral and 28% disagreed. CONCLUSIONS: The QUIP is educational to radiologists and trainees, leading to positive changes in clinical practice. The majority accepts this program but there is still a stigma felt when a QUIP is received, particularly among residents. Nevertheless, we feel that QUIP has been integrated into our radiology culture and, hopefully, imminent transition to commercial quality software will be smooth.
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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 | 0.029 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| 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".