Radiographic image interpretation by Australian radiographers: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Radiographer image evaluation methods such as the preliminary image evaluation (PIE), a formal comment describing radiographers' findings in radiological images, are embedded in the contemporary radiographer role within Australia. However, perceptions surrounding both the capacity for Australian radiographers to adopt PIE and the barriers to its implementation are highly variable and seldom evidence-based. This paper systematically reviews the literature to examine radiographic image interpretation by Australian radiographers and the barriers to implementation. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses were used to systematically review articles via Scopus, Ovid MEDLINE, PubMed, ScienceDirect and Informit. Articles were deemed eligible for inclusion if they were English language, peer-reviewed and explored radiographic image interpretation by radiographers in the context of the Australian healthcare system. Letters to the editor, opinion pieces, reviews and reports were excluded. RESULTS: A total of 926 studies were screened for relevance, 19 articles met the inclusion criteria. The 19 articles consisted of 11 cohort studies, seven cross-sectional surveys and one randomised control trial. Studies exploring radiographers' image interpretation performance utilised a variety of methodological designs with accuracy, sensitivity and specificity values ranging from 57 to 98%, 45 to 98% and 68 to 98%, respectively. Primary barriers to radiographic image evaluation by radiographers included lack of accessible educational resources and support from both radiologists and radiographers. CONCLUSION: Australian radiographers can undertake PIE; however, educational and clinical support barriers limit implementation. Access to targeted education and a clear definition of radiographers' image evaluation role may drive a wider acceptance of radiographer image evaluation in Australia.
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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 | 0.030 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".