A qualitative study exploring the diagnostic and treatment journeys of children and young people with gastroduodenal disorders of gut-brain interaction, their families, and the clinicians who care for them
Bibliographic record
Abstract
Abstract Background Gastroduodenal disorders of gut-brain interaction (DGBI) are prevalent in the paediatric population. Diagnostic pathways and subsequent treatment management approaches for children and young people can be highly variable, leading to diverse patient and clinical experiences. This study explores the DGBI diagnostic experiences of children and their families and the perspectives of clinicians in the New Zealand context. Methods Semi-structured interviews were conducted with 12 children with gastroduodenal DGBIs and their families and clinicians who care for children with DGBIs. Interviews were recorded, transcribed, and narratively analysed. Results Five children and family themes emerged: 1) how it all started, 2) the impacts symptoms had on child and family life, 3) their experiences with testing and investigations, 4) the perceptions and impacts of challenging clinical relationships, and 5) the uncertainness of trial and error treatments. Clinicians also identified five key themes: 1) navigating the complexity of presenting symptomology, 2) the challenging diagnostic investigation decision-making process, 3) navigating management and treatment approaches, 4) a lack of standardised clinical pathways, and 5) establishing therapeutic relationships with patients and families. Conclusion Children, their families, and clinicians confirmed the clinical complexity of DGBIs, the challenges of diagnosis and management, and the stress this places on therapeutic relationships. Clearer diagnostic pathways and new investigations that could provide improved identification and discrimination of DGBIs are needed to minimise the treat-test repeat cycle of care and improve health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".