Contemporary clinical conversations about stuttering: What does brain imaging research mean to clinicians?
Bibliographic record
Abstract
PURPOSE: To discuss among neuroscientists and community speech-language pathologists what brain imaging research means to clinicians. METHOD: Two university neuroscientists and two speech-language pathologists in private practice discussed the matter. Written conversational turns in an exchange were limited to 100 words each. When that written dialogue was concluded, each participant provided 200 words of final reflection about the matter. RESULT: For now, neuroscience treatments are not available for clinicians to use. But sometime in the future, a critical mass of neuroscientists will likely produce such treatments. The neuroscientists expressed diverse views about the methods that might be used for that to occur. CONCLUSION: Neuroscience does have practical clinical application at present and, in a way, that does not exclude a concurrent influence of the social model of disability. As such, the current practices of clinicians are supported by basic neuroscience research.
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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 | 0.051 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".