Predicting the effectiveness of Test of Infant Motor Performance as an early marker of motor development delay in preterm infants
Bibliographic record
Abstract
Objectives: The purpose of this study was to determine the effectiveness of Test of Infant Motor Performance (TIMP) in detecting motor developmental delay in preterm infants. Material and methods: The present study was conducted on 30 infants born preterm with corrected age of 2 months and follow up of TIMP at 4 months corrected age and Alberta Infant Motor Scale (AIMS) at 2, 4 and 6 months corrected age. Study period was January 2012 to December 2015. Results: Pearson product moment correlation coefficient used to assess the relationship between the raw scores of TIMP and AIMS percentilerank at corrected age of 2 and 4 months, was 0.757 (p<0.0001) and 0.874 (p<0.0001) respectively. An analysis of the sensitivity, specificity, positive predictive value and negative predictive value of various TIMP cutoff scores for comparison with AIMS scores above and below the 10th percentile revealed the best TIMP score with a cutoff of -1 standard deviation below the mean. The results for comparison of 2, 4, 6 months corrected age AIMS data using Pearson chi square test was highly significant with p 0.001 at 2months CA AIMS data with 4 months CA AIMS data. Conclusion and Interpretation: TIMP and AIMS are equally useful in the assessment of infant motor performance at 4 months of corrected age. A cutoff score of -1 SD from the mean on TIMP was a better predictor of developmental outcome in this study.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".