The association between guideline adherent radiographic imaging by chiropractic students and the diagnostic yield of clinically significant findings.
Bibliographic record
Abstract
BACKGROUND: Radiographic guidelines aim to increase the diagnostic yield of clinically relevant imaging findings whilst minimising risk. This study assessed the appropriateness of radiographic referrals made by student chiropractors and explored the association between guideline appropriate imaging and clinically significant radiographic findings. METHODS: Radiographic referral and report findings (n=437) from 2018 were extracted from Macquarie University chiropractic clinics. Appropriateness of radiographic referrals was assessed according to current radiographic guidelines. Radiographic findings were assessed for clinical significance. The association between guideline appropriate radiographic referral and clinically significant radiographic findings was assessed using logistic regression analysis and odds ratios were estimated. RESULTS: The proportion of guideline appropriate imaging was 55.8% (95%CI: 51.2-60.4). An association between guideline appropriate radiographs and clinically significant findings was found (OR: 2.2; 95%CI: 1.3-4.1). CONCLUSIONS: Approximately half of all radiographic referrals made by chiropractic students were guideline concordant. Guideline appropriate imaging was associated with an increase in clinically significant radiographic findings.
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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 | 0.004 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".