Internal Consistency and Floor/Ceiling Effects of the Gross Motor Function Measure for Use with Children Affected by Cancer: A Cross-Sectional Study
Bibliographic record
Abstract
Children/adolescents with cancer can develop adverse effects impacting gross motor function. There is a lack of gross motor function assessment tools that have been validated for this population. The aim of this multicenter cross-sectional study was to preliminary validate the 88-item Gross Motor Function Measure (GMFM-88) for use in children/adolescents with cancer, exploring internal consistency and floor/ceiling effect. Inclusion criteria regarded children/adolescents diagnosed with cancer on treatment or <1 year off therapy. The internal consistency was assessed using Cronbach's α, and the floor-ceiling effects were calculated through percentage. This study involved 217 participants with heterogeneous neoplasm conditions. Internal consistency was good, with a Cronbach's α of 0.989. Floor-ceiling effect analysis reveals that several items obtained a dichotomous scoring distribution in each of the five sub-scales of the GMFM-88. This can be explained by the heterogeneous clinical characteristics of the target population. The preliminary validation of GMFM-88 in a group of children/adolescents affected by cancer suggests that some items are not able to discriminate between different gross motor function levels, and therefore it does not represent an informative tool to measure gross motor function in children with cancer. Future research is needed to define which ones could be more useful for clinical practice.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".