Bibliographic record
Abstract
ity, 3 especially with MND.IMP scores were related to neurological condition, therefore, assessment results could assist with diagnosis of severe motor delays and complex MND.Early detection of complex MND could assist in diagnosing DCD and borderline cerebral palsy before impairments in quality of movement translate into functional performance deficits.DCD may not be diagnosed until children are 5 years or older and have entered the school system, if it is diagnosed at all. 4 If the IMP can indeed identify children with MND and DCD in young infants and children, it would fill a much needed gap in assessment tools available to the clinician.Early education and appropriate intervention could then possibly prevent future activity limitations.Overall, the IMP looks to be a promising option for assessing infant motor performance as well as quality of movement.However, further development and standardization of IMP scores is required.Normative data need to be collected in order to interpret scores and use the assessment for discriminative purposes.Development of cut-off scores is also needed in order to investigate sensitivity and specificity of assessment interpretations.Clinical utility, including ease of administration, also requires further investigation.As with other videotaped assessments, even experienced clinicians may require training or certification on administration, scoring, and interpretation of the IMP due to the subjective nature of the quality and neural recruitment items.As with all assessments, especially with younger children, sequential testing over time may be needed to assess accurately quality of movement and motor performance.Clinically, the AIMS may continue to be a useful screening tool, while the IMP may be used for a more thorough assessment when quality of movement is questionable.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".