Cross-cultural adaptation of the Gross Motor Function Classification System into Brazilian-Portuguese (GMFCS).
Bibliographic record
Abstract
BACKGROUND: Due to the complexity of clinical symptoms of cerebral palsy and the difficulties in classifying it based upon the motor types and the topography of the body distribution only, Canadian researchers have proposed the Gross Motor Function Classification System (GMFCS). Although this classification system has been largely used in Brazil, it has not been cross culturally adapted yet. OBJECTIVES: To perform the cross adaptation of the Gross Motor Function Classification System for the Cerebral Palsy (GMFCS) into Brazilian-Portuguese and to verify the reliability among observers of the adapted instrument in Brazilian children. METHODS: This study was performed in two stages; the first stage was related to the process of cross-cultural adaptation and the second stage tested the instrument. Translation, back-translation, semantic and content analysis, back-translation of the final version and the approval of the authors were used for the cross-cultural adaptation. The test of the instrument was performed in 40 children with cerebral palsy, who were evaluated by two raters to verify the reliability among the observers. RESULTS: The results showed that the stages of translation and back-translation did not present any difficulties and the semantic and conceptual equivalence was achieved. The reliability among the observers showed that the evaluations do not differ and that there is an excellent correlation and internal consistency of the construct with an ICC of 0.945 (95% CI 0.861 to 0.979) and a Cronbach a of 0.972. CONCLUSIONS: The final version of the GMFCS showed good potential of applicability for undergraduate students and professionals of the neuropediatric area.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".