Research Priorities for Childhood Apraxia of Speech: A Long View
Bibliographic record
Abstract
Special Issue: Selected Papers From the 2022 Apraxia Kids Research Symposium. The field of childhood apraxia of speech (CAS) has developed significantly in the past 15 years, with key improvements in understanding of basic biology including genetics, neuroscience, and computational modelling; development of diagnostic tools and methods; diversity of evidence-based interventions with increasingly rigorous experimental designs; and understanding of impacts beyond impairment-level measures. Papers in this special issue not only review and synthesize the some of the substantial progress to date but also present novel findings addressing critical research gaps and adding to the overall body of knowledge. A second aim of this prologue is to report the current research needs in CAS, which arose from symposium discussions involving researchers, clinicians, and Apraxia Kids community members (including parents of children with CAS). Four primary areas of need emerged from discussions at the symposium. These were: (a) What questions should we ask? (b) Who should be in the research? (c) How do we conduct the research? and (d) How do we move from research to practice? Across themes, symposium attendees emphasized the need for CAS research to better account for the diversity of people with CAS and improve the timeliness of implementation of high-level evidence-based practice across the lifespan. It is our goal that the articles and prologue discussion in this special issue provide an appreciation of advancements in CAS research and an updated view of the most pressing needs for future research.
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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.029 | 0.014 |
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".