Predictive validity of Prechtl’s Method on the Qualitative Assessment of General Movements: a systematic review of the evidence
Bibliographic record
Abstract
AIM: The aim of this systematic review was to examine the evidence for the predictive validity of Prechtl's Method on the Qualitative Assessment of General Movements (GMsA) with respect to neurodevelopmental outcomes. METHOD: Six electronic databases (PsychINFO, Embase, Health and Psychosocial Instruments, PubMed, and AMED) were searched using the following keywords to identify all studies that examined the predictive validity of the GMsA: 'general movements', 'assessment', 'movement', 'child development', 'infant', and 'predictive value of test'. Only English- and French-language studies were included, whereas studies that focused on spontaneous mobility in preterm infants, but not necessarily the GMsA, or which did not report on the predictive value of the GMsA were excluded. A total of 39 studies were included in the final analysis. RESULTS: Studies were separated according to the age at follow-up: 12 to 23 months, 2 to 3, 4 to 11, and 12 to 18 years. All used a longitudinal cohort study design; however, the outcome measures differed greatly amongst the studies. Values for sensitivity, specificity, positive predictive value, and negative predictive value varied amongst studies. The overall trend indicated that the presence of abnormalities in the quality of fidgety movements at 12 weeks adjusted age is more predictive of adverse outcomes than abnormal writhing movements. INTERPRETATION: The GMsA demonstrates potential as a cost-effective, non-intrusive means of infant examination. However, current studies include important sources of bias. Future methodologically rigorous studies with functional outcomes are suggested.
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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 | 0.071 | 0.262 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".