Risk factors for pain in children with severe cognitive impairments
Bibliographic record
Abstract
Diagnosing cause of pain in children with severe cognitive impairments is difficult due to their problems with communication. Identification of risk factors for specific pain etiologies might help professionals in this task. The aim of this study was to determine whether child-related characteristics increase risk for specific types of pain. Participants were the caregivers of 41 females and 53 males with moderate to profound mental retardation, who were aged 3 to 18 years 8 months (mean 10:1, SD 4:4) but who communicated at the level of a typical child of 13.8 months (SD 10 months): 44 of the children had cerebral palsy (CP) and 59 a seizure disorder. Caregivers reported the cause of children's episodes of pain for four 1-week periods over 1 year. Logistic regression analyses were used to predict occurrence of specific types of pain using children's demographic, medical, and physical characteristics. Children had 406 episodes of pain due to accident, gastrointestinal conditions, musculoskeletal problems, infection, recurrent conditions, and common childhood causes. Results indicated that a unique set of risk factors predicted each pain type in this sample. Significant risk factors for pain included: lack of visual impairment and leg impairment (accidental pain); seizures, leg impairment, and greater number of medications (non-accidental pain); being male and tube fed (musculoskeletal pain); age <7 years, absence of CP, visual impairment, and less frequent medical monitoring (infection pain); being female and with arm impairment (gastrointestinal pain); and being tube fed and taking fewer medications (common childhood pains). In most cases, models were more specific than sensitive, indicating that the significant predictors are more useful for eliminating potential pain causes. These results suggest that population risk factors may be helpful in structuring diagnostic investigations for individual children with severe cognitive impairments.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".