Bibliographic record
Abstract
We were excited to read the September 2001 issue of Physical Therapy in which Ketelaar et al reported the outcomes of a randomized controlled clinical trial on functional therapy in children with cerebral palsy (CP).1 To our knowledge, this is the first study ever published on functional therapy in pediatric physical therapy. We would like to emphasize the value of evidence showing improvements in “activities” as compared with evidence showing changes in impairments. Ketelaar and colleagues reported that therapy designed to enhance function has positive effects on performance of daily functional motor skills as measured by the Pediatric Evaluation of Disability Inventory (PEDI). However, no differences between the intervention group and the reference group were found for improvement in basic gross motor abilities such as standing, walking, running, and jumping. In the “Discussion” section, the authors addressed the differences in the types of outcome measurements provided by the PEDI and the Gross Motor Function Measure (GMFM), such as differences in the method of administration (interview of the parent [PEDI] versus observation of the child [GMFM]) and the setting (performance in the daily environment [PEDI] versus a standardized environment at the therapist's clinic [GMFM]).
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 | 0.018 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.129 | 0.053 |
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".