Diagnostic Imaging Guidelines Implementation Study for Spinal Disorders: A Randomized Trial with Postal Follow-ups*
Bibliographic record
Abstract
PURPOSE: Implementation strategies of imaging guidelines can assist in reducing the number of radiographic examinations. This study aimed to compare the perceived need for diagnostic imaging before and after an educational intervention strategy. METHODS: One hundred sixty Swiss chiropractors attending a conference were randomized to either receive a radiology workshop, reviewing appropriate indications for diagnostic imaging for adult spine disorders (n = 80), or be in a control group (CG). One group of 40 individuals dropped out from the CG due to logistic reasons. Participants in the intervention group were randomly assigned to three subgroups to evaluate the effect of an online reminder at midpoint. All participants underwent a pretest and a final test at 14-16 weeks. A posttest was administered to two subgroups at 8-10 weeks. RESULTS: There was no difference between baseline scores, and overall scores for the pretest and the final tests for all four groups were not significantly different. However, the subgroup provided with access to a reminder performed significantly better than the subgroup with whom they were compared (F = 4.486; df = 1 and 30; p = .043). Guideline adherence was 50.5% (95% CI, 39.1-61.8) for the intervention group and 43.7% (95% CI, 23.7-63.6) for the CG at baseline. Adherence at follow-up was lower, but mean group differences remained insignificant. CONCLUSIONS: Online access to specific recommendations while making a clinical decision may favorably influence the intention to either order or not order imaging studies. However, a didactic presentation alone did not appear to change the perception for the need of diagnostic imaging studies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".