Using an Electronic Pain Diary to Better Understand Pain in Children and Adolescents with Arthritis
Bibliographic record
Abstract
UNLABELLED: Summary AIM: To explore the pain experience of youths (9-18 years old) with juvenile idiopathic arthritis, using a real-time data capture electronic pain diary. MATERIALS & METHODS: A descriptive study design with repeated measures was used. A total of 76 youths, aged 9-18 years old with juvenile idiopathic arthritis were recruited from a Canadian pediatric tertiary care center and asked to record their pain three times a day for 2 weeks using the e-Ouch(©) pain diary. RESULTS: On average, participants reported mild levels of pain intensity, unpleasantness and interference, as well as stiffness and mild-to-moderate levels of fatigue. Interference of stiffness and pain with activities of daily living were significantly higher in the morning versus the afternoon and evening; while fatigue was significantly higher in the morning and evening compared with the afternoon. CONCLUSION: Real-time data capture approaches can be used enable a better understanding of how pain and other symptoms in youths with juvenile idiopathic arthritis change within and across days, and how best to treat them. Valuable next steps include incorporating the e-Ouch pain diary into an everyday clinical setting to measure patient outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".