Physical function estimates change in pain following <scp>IIPT</scp> among children with chronic pain
Bibliographic record
Abstract
INTRODUCTION: Chronic pain can negatively impact a child's quality of life. Pediatric Intensive Interdisciplinary Pain Treatment (IIPT) programs aim to improve overall functioning despite pain through various rehabilitative strategies. It is, however, unclear whether improved function corresponds to self-reported decrease in pain levels. Hence, the purpose of this study is to examine the relationship between changes in physical function and perceived pain among children with chronic pain who have undergone inpatient IIPT. MATERIALS AND METHODS: A secondary analysis of pre-existing databases of IIPT from two different inpatient acute rehabilitation programs was carried out. Children and adolescents (N = 309; age = 16.2 ± 2.6; 79% females) with chronic pain who attended on average 4-week inpatient IIPT from Nov 2011 to Jan 2023 were included. Participants completed pain intensity (Numerical Pain Rating Scale) and self-reported function measures (Lower Extremity Functional Scale [LEFS], Upper Extremity Functional Index [UEFI], Canadian Occupational Performance Measure [COPM]-Performance, and COPM-Satisfaction) at admission and discharge. RESULTS: Change in self-reported physical function was significantly associated with change in pain from admission to discharge. After covariate adjustment, self-reported physical function (per the LEFS, UEFI, COPM-Performance, and COPM-Satisfaction) explained 19.8%, 7.8%, 12.0%, and 8.6% of the variance in change in pain, respectively. These measures of self-reported physical function further distinguished between minimal (<30%) and moderate (≥30%) pain reduction. CONCLUSIONS: Self-reported functional gains during IIPT are associated with greater change in perceived pain. Moreover, measures of self-reported physical function can help identify children at risk of minimal pain reduction post-IIPT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".