Stability of parent‐reported manual ability and gross motor function classification of cerebral palsy
Bibliographic record
Abstract
Christine Imms and her co-authors1 are to be commended on their study examining the stability of caregiver reported classification of function of children with cerebral palsy (CP) using the Gross Motor Function Classification System (GMFCS) and the Manual Ability Classification System (MACS). Communication among clinicians and researchers has been augmented immensely with the use of the GMFCS, and a similar benefit appears to be emerging for manual ability with the uptake of the MACS. This topical and clinically relevant study found that parental rating of the GMFCS and the MACS was generally stable over 12 months in a group of children aged 11 to 12 years. The paper raises a number of important issues with respect to the properties and uses of classification systems. The GMFCS is now widely recognized and used as a means of categorizing motor function in children with CP. It is reliable, valid, and stable over time.2, 3 The MACS is a recently developed classification tool describing children's ability to handle objects in daily life.4 There is some evidence for the reliability and validity of the MACS, however, the paper by Imms et al. is the first to examine its stability, as well as adding to previous work on the stability of the GMFCS. Classification systems can be used to provide a common language for communication among clinicians, researchers, and caregivers, and may guide treatment decisions and assist with goal setting. Rather than being prescriptive they can be used to guide clinicians to ensure children receive treatment appropriate for their age and level of function. The development of the motor growth curves5 allows for realistic goal setting with the child and family. The GMFCS and MACS both have five levels and are designed to be simple yet reliable. At times it may be difficult to categorize children because they appear to fit between two levels. The answer is not to add more levels because the potential trade-off with added complexity is reduced reliability. The GMFCS and the MACS are not outcome measures and neither is designed or meant to measure change.6 Consequently they require a level of stability, i.e. the expectation that the characteristic in question will not change significantly and the child will remain in the same category over time. By contrast, reliability concerns the consistency of measurement, with potential sources of error coming from the tool or the user. However, for a tool to be stable it first needs to be reliable. The study by Imms et al. found greater stability for the GMFCS than for the MACS, stating as a possible explanation that 'the content of the GMFCS is simpler to understand'. As noted by the authors, stability of the GMFCS was higher in a recent study2 compared with an earlier study.3 Is it that clinicians and parents require some familiarization of these classification systems over time to enhance stability? And does their reliability improve with increased familiarization? Another point to consider is that this study uses parental report only. Reliability and/or stability for the MACS may be different for parents compared with clinicians. Another threat to stability is that children truly do change. For example, children may be classified down one GMFCS level following major surgical interventions or when they have significant musculoskeletal deformities left untreated. This is particularly relevant for the age group studied in the Imms et al. paper. These exceptions do not undermine the overall stability of the tool but may explain some of the disparity observed in selected age groups. Imms et al. correctly state that the main limitations of their study included looking only at children aged 11 to 12 years and assessing them only twice, 12 months apart. Further work examining the stability of the MACS in a wider range of age groups and over longer periods of time with more assessment points is required, as has been carried out with the GMFCS.2 This of course begs the question, what is an appropriate length of time to examine 'stability' of a characteristic in question? The GMFCS and MACS provide complementary information on motor function in children with CP, as seen by their high but not perfect correlation in this study. We can look forward to the routine use of the MACS along with the GMFCS, as well as reliable and valid outcome measures of function to address a wide range of clinical and research questions.
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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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".