Trends in diagnostic tests ordered for children: a retrospective analysis of 1.7 million laboratory test requests in Oxfordshire, UK from 2005 to 2019
Bibliographic record
Abstract
Objective To better understand testing patterns in children, we measured temporal trends in paediatric testing from 2005 to 2019 in Oxfordshire, UK. Design Descriptive study of population-based secondary data. Setting Oxfordshire University Hospitals National Health Service Trust laboratories. Participants Children aged 0–15 years in Oxfordshire who received at least one blood test. Main outcome measures We estimated average annual percentage changes (AAPCs) in test use using joinpoint regression models. Temporal changes in age-adjusted rates in test use were calculated overall and stratified by healthcare setting, sex, and age. Results Between 2005 and 2019, 1 749 425 tests were performed among 113 607 children. Overall test use declined until 2012, when test rates appeared to increase (AAPC 1.5%, 95% CI −0.8% to 3.9%). Most tests were performed in inpatient settings, where testing rates stayed steady (AAPC −0.6%, 95% CI −2.1% to 0.9%). Increases were highest in females, those aged 6–15 years and in the outpatient setting. The greatest increase in testing was for vitamin D (AAPC 26.5%), followed by parathyroid hormone (9.8%), iron studies (9.3%), folate (8.4%), vitamin B 12 (8.4%), HbA1c (8.0%), IgA (7.9%) and coeliac (7.7%). Conclusions After an initial decline, laboratory test use by children in Oxfordshire demonstrated an apparent increase since 2012. Test use increased in outpatient and general practice settings, however remained steady in inpatient settings. Further research should examine the root causes and implications for test increases, and whether these increases are warranted. We encourage clinicians to consider the individual and systemic implications of performing blood tests in children.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".