The Israeli Rett Syndrome Center. Evaluation and Transdisciplinary Play-Based Assessment
Bibliographic record
Abstract
Rett syndrome (RS) is a neuro-developmental syndrome of genetic origin, which mainly affects women. Individuals diagnosed with RS exhibit a variety of functional difficulties, which impair their quality of life. The variety of impairments and the differences between each child makes it necessary to administer skilled treatment, individually tailored to each client. Since the foundation of proper treatment is based on a structured, well administered, insightful assessment, the individual with RS with her complex array of difficulties should benefit from such a procedure. This notion has led to the establishment of the Israel Rett Syndrome Center. The center includes a medical branch located at the Safra Shildren's Medical Center at Tel Hashomer and an education/rehabilitation team, who performs assessments in special education facilities and residential settings throughout Israel. The assessment team works by means of arena assessment according to the concept of play-based assessment. This article presents the working model used by the education/rehabilitation team at the Israeli Rett Syndrome Center. The principles and working characteristics of the Israel Rett Syndrome Center team are suggested here as a potential model for establishing additional teams, presenting similar evaluation services for other individuals with RS as well as for analogous populations.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".