Report of the Consensus Conference on the Diagnosis of Auditory Processing Disorders in School-Aged Children
Bibliographic record
Abstract
BACKGROUND A group of 14 senior scientists and clinicians met at the Callier Center in Dallas over the 2–day period, April 27–28, 2000, in an attempt to reach a consensus on the problem of diagnosing auditory processing disorders in school-aged children. The conference was organized by James Jerger and Frank Musiek. The following individuals participated: Sharon Abel, PhD, University of Toronto, Toronto, ON Jane Baran, PhD, University of Massachusetts, Amherst, MA Anthony Cacace, PhD, Albany Medical College, Albany, NY Gail Chermak, PhD ‡, Washington State University, Pullman, WA Susan Dalebout, PhD, University of Virginia, Charlottesville,VA Jay Hall III, PhD, University of Florida, Gainesville, FL Linda Hood, PhD, Louisiana State University Medical Center, New Orleans, LA Lisa Hunter, PhD, University of Minnesota, Minneapolis, MN James Jerger, PhD, University of Texas at Dallas, Dallas, TX Susan Jerger, PhD, University of Texas at Dallas, Dallas, TX Robert Keith, PhD, University of Cincinnati, Cincinnati, OH Frank Musiek, PhD, Dartmouth-Hitchcock Medical Center, Hanover, NH Ross Roeser, PhD, University of Texas at Dallas, Dallas, TX Christine Sloan, PhD, Annapolis Valley Regional School Board Berwick, NS Meeting both as separate groups and in plenary session, the conferees reached the consensus summarized below.
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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 | 0.164 | 0.132 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.013 | 0.009 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".