Prevalence and management of symptom diagnoses in children in general practice
Bibliographic record
Abstract
OBJECTIVE: To explore the prevalence of symptom diagnoses in children in general practice and the management strategies performed by GPs. DESIGN: Retrospective cohort study. SETTING: The Netherlands. PARTICIPANTS: Participant data registered in a Dutch practice-based primary care research network (Family Medicine Network [FaMe-Net]). MAIN OUTCOME MEASURES: All episodes of care with at least 1 contact for a symptom diagnosis in 2018 as well as management strategies within each episode of care including the number and type of diagnostic interventions, therapeutic interventions, and referrals. RESULTS: Overall, 6162 children under 15 years of age and registered with GP practices were included in the cohort. Among them, 2767 (44.9%) had at least 1 contact with their GP for at least 1 symptom diagnosis, and 161 (2.6%) had at least 1 persistent symptom diagnosis. Constipation, wheezing, and weakness were the most commonly found persistent symptoms. For persistent symptom diagnoses, GPs indicated more therapeutic interventions (n=217, 40.1%) compared to diagnostic interventions (n=175, 32.3%) or referrals (n=149, 27.6%). CONCLUSION: Symptom diagnoses are highly prevalent in children in general practice. Future research should focus on which children are at risk of developing persistent symptom diagnoses and how to manage them.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".