Something Else Going On? Diagnostic Uncertainty in Children with Chronic Pain and Their Parents
Bibliographic record
Abstract
Diagnostic uncertainty, the perceived lack of an accurate explanation of the patient’s health problem, remains relatively unstudied in children. This study examined the prevalence, familial concordance, and correlates of diagnostic uncertainty in children and their parents presenting to a multidisciplinary pain clinic in the United States. One hundred and twenty-six parents and 91 of their children (Mage = 13.93 years, range = 8–18 years) completed a brief three-item measure of diagnostic uncertainty, as well as measures of pain-related distress and functioning. Forty-eight percent of children and 37% of parents believed something else was going on with the child’s pain that doctors had not found out about yet. Across the three items, 66%–77% of children and their parents agreed in their endorsement of diagnostic uncertainty. Parents who believed that something else was going on with their child’s pain had children with higher avoidance of pain-related activities (F = 5.601, p = 0.020) and lower pain willingness (F = 4.782, p = 0.032). Neither parent nor child diagnostic uncertainty was significantly related to the child’s pain-related functioning. Diagnostic uncertainty, particularly in parents, is relevant in the experience of pediatric chronic pain and warrants further investigation as both a risk factor and therapeutic target.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".