The Clinical Course of Pain Intensity in Children and Youth with Cerebral Palsy
Bibliographic record
Abstract
Cerebral palsy (CP) is a permanent neurological disorder affecting movement and posture caused by disturbances in the fetal or infant brain. Although pain is a commonly reported symptom among individuals with CP, little is known about how pain varies in this population. This thesis aimed to identify short-term pain trajectories and their associations with well-being in children and youth with CP. First, I conducted a systematic review to identify knowledge gaps and describe the clinical course of pain intensity in persons with CP resulting from usual care or specific interventions. I found six moderate-to-high quality studies describing pain trajectories in this population. The review suggests that pain trajectories evolve differently in sub-groups depending on clinical or usual care and pain chronicity. Second, I designed a cohort study and tested its feasibility in a cohort of 10 children/youth with CP attending two Ontario children’s treatment centers. I assessed the feasibility of conducting a larger cohort study by measuring recruitment and participation, attrition, data completion and barriers to study success. I had a 50% recruitment rate, 90% follow-up rate, and minimal missing data. The feasibility indicators supported the conduct of a larger study. Third, I conducted a cohort study including 102 children/youth to identify short-term pain trajectories and their association with well-being. I identified five distinct trajectories, three of which were stable over five weeks. Approximately 32% had moderate to high pain intensity trajectories. Those with higher pain intensity reported lower mean physical well-being. Those in trajectories with an estimated pain intensity greater than 3.6/10 at baseline had lower mean psychological well-being regardless of their trajectory group. However, the precision of the measures of association varies. In summary, my thesis advances the study of pain in youth with CP by providing information about short-term pain intensity trajectories and their association with well-being. I synthesized the existing literature about the clinical course of pain intensity and identified how pain trajectory membership is associated with physical and psychological well-being. Future work is needed to examine trajectories of longer duration and the associations between pain trajectories and other outcomes including sleep quality and health comorbidities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".