Association between school absence and physical function in paediatric chronic fatigue syndrome/myalgic encephalopathy
Bibliographic record
Abstract
OBJECTIVE: To investigate factors associated with school attendance and physical function in paediatric chronic fatigue syndrome/myalgic encephalopathy (CFS/ME). DESIGN: Cross-sectional study. SETTING: Regional specialist CFS/ME service. PATIENTS: Children and young people aged under 18 years. OUTCOME MEASURES: Self-reported school attendance and physical function measured using the physical function subscale of the Short Form 36. METHODS: Linear and logistic regression analysis of data from self-completed assessment forms on children attending a regional specialist service between 2004 and 2007. Analyses were done in two groups of children: with a completed Spence Children's Anxiety Scale (SCAS) and with a completed Hospital Anxiety and Depression Scale (HADS). RESULTS: Of 211 children with CFS/ME, 62% attended 40% of school or less. In children with completed SCAS, those with better physical function were more likely to attend school (adjusted odds ratio (OR) 1.70; 95% CI 1.36 to 2.13). This was also true for those with completed HADS (adjusted OR 2.05; 95% CI 1.4 to 3.01). Increasing fatigue and pain and low mood were associated with worse physical function. There was no evidence that anxiety, gender, age at assessment, family history of CFS/ME or time from onset of symptoms to assessment in clinic were associated with school attendance or physical function. IMPLICATIONS: Paediatricians should recognise that reduced school attendance is associated with reduced physical function rather than anxiety. Improving school attendance in children with CFS/ME should focus on evidence-based interventions to improve physical function, particularly concentrating on interventions that are likely to reduce pain and fatigue.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".