Benchmarking non-attendance patterns in paediatric medical imaging: A retrospective cohort study spotlighting First Nations children
Bibliographic record
Abstract
INTRODUCTION: Non-attendance at Medical Imaging (MI) appointments can result in inefficiencies in healthcare resource allocation, increased financial burdens, and lead to potential barriers to effective healthcare delivery. We evaluated factors associated with non-attendance of MI appointments for children including variables: gender; age groups; residential postcodes; Indigenous status; appointment dates; appointment reminders and socio-economic status. METHOD: Retrospective cohort study of children with scheduled MI appointments at a Tertiary paediatric hospital in Australia, between January and December 2022. Data were extracted from the Radiology Information System and integrated with socio-economic census data through linking with postcode. Chi-squared, and logistic regression analysis were performed to identify significant predictors of non-attendance. RESULTS: Out of 17,962 scheduled outpatient appointments, 6.2 % did not attend. Males were less likely to attend than females (7.3 % vs. 5.8 %; p < 0.001). Older children had the highest frequency of non-attendance (p < 0.001). First Nations identified children had a higher likelihood of non-attendance at 14.5 % compared to non-First Nations at 5.8 %, and the odds ratio (OR) of First Nation children not attending was 2.54 (CI 2.13-3.03; p < 0.001) higher than non-First Nations children. Children from areas of disadvantage were less likely to attend (p < 0.001). Bone mineral densitometry had the highest odds of non-attendance (19.4 % of bookings) compared to other imaging modalities (p < 0.001). CONCLUSION: The following characteristics were associated with non-attendance: older male gender, residing in areas of socio-economic disadvantage, or identifying as First Nations Australians. By reviewing these findings with the cultural and professional experience of our Indigenous co-author, we have identified some strategies for improving attendance amongst First Nations children. IMPLICATIONS FOR PRACTICE: Factors associated with non-attendance, or "missed opportunities for care", provide opportunities for intervention to improve attendance for vulnerable groups of children who require medical imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".