Criterion validity of the <scp>GMFM</scp>‐66 item set and the <scp>GMFM</scp>‐66 basal and ceiling approaches for estimating <scp>GMFM</scp>‐66 scores
Bibliographic record
Abstract
AIM: The aim of this study was to compare the accuracy of two abbreviated approaches for estimating Gross Motor Function Measure 66 (GMFM-66) scores against the full GMFM-66 and to explore their strengths and limitations. METHOD: An existing dataset (n=224) comprising children aged 1 to 13 years (mean age 6y 11mo, SD 4y 6mo; 132 males, 92 females) with cerebral palsy (CP) of all Gross Motor Function Classification System (GMFCS) levels was used to compare the validity of the item set version (GMFM-66-IS) and the basal and ceiling version (GMFM-66-B&C) with the full GMFM-66 scores. Follow-up assessment at 1 year (n=109) allowed evaluation of change scores and accuracy at a single point in time. RESULTS: The cross-sectional agreement was excellent for both abbreviated measures (all intraclass correlation coefficients [ICCs] >0.98). When measuring change over time, both the GMFM-66-IS and the GMFM-66-B&C showed good agreement for children with bilateral CP (ICCs >0.9). However, the GMFM-66-IS assessed change over 1 year more accurately than the GMFM-66-B&C in children with unilateral CP (ICC=0.89 vs ICC=0.58; 95% confidence intervals do not overlap). INTERPRETATION: Both approaches for estimating GMFM-66 scores are accurate at a single point in time. If the primary goal of assessment is to measure change, the full GMFM-66 should still be regarded as the criterion standard. The GMFM-66-IS should be the preferred shortened measure for children with unilateral CP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".