Pain Experiences of Children and Adolescents With Osteogenesis Imperfecta
Bibliographic record
Abstract
OBJECTIVE: Pain is a commonly experienced symptom for children and adolescents diagnosed with osteogenesis imperfecta (OI). The purpose of this integrative review was to describe the pain experience of children and adolescents with OI as well as critically appraise the content and methods of studies assessing OI pain. METHODS: Five electronic bibliographic databases were searched. Published quantitative, qualitative, and/or mixed-method studies assessing pain in children and adolescents with OI were included and appraised. Constant comparison of the extracted data was used to synthesize themes. RESULTS: A total of 783 titles were identified, and 19 studies that met the inclusion criteria were included in this review. Study appraisal scores ranged from 25.0% to 83.3% using the Quality Assessment Tool. The majority of studies included assessed pain as a secondary outcome (63%) and less than half used moderately established or well-established tools (42%). Two themes were uncovered: "Pain is Present and Problematic" and "Issues with Pain Assessment." Key findings under each theme include: (1) the negative impacts of pain and the substandard use of pain management strategies; and (2) the lack of multidimensional and consistent pain assessments, as well as difficulties in assessing pain in younger children. DISCUSSION: Research on OI has focused very little on pain experience in children and adolescents, and there is no standard method of assessing pain. To better describe the pain experience of these patients, future research should focus on better characterizing OI pain with the use of age-appropriate valid, reliable, and multidimensional pain assessment tools.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".