Computerized algorithms to score P1 wave characteristics in the cortical auditory evoked potentials of children with cochlear implants
Bibliographic record
Abstract
The purpose of this project was to develop a computerized algorithm to score the latency and wave width of the P1 component in the cortical auditory evoked potentials (CAEPs) of normal-hearing children and children with cochlear implants. The primary analytical tool used in the project was local polynomial smoothing, as implemented in the KernSmooth package for S-Plus software (Wand & Jones, 1995). The thesis provides background on cochlear implantation, CAEPs, and local polynomial smoothing, then describes development of an S-Plus program to score P1 latency and wave width. The P1 latency scores assigned by the program were found to correlate highly (ICCs > .90) with scores assigned by expert audiologists. Research results based on computer-assigned scores were similar to results based on expert-assigned scores (Sharma et al., 2002a, 2002b, 2005). Additional analyses are reported concerning the interrelationship of P1 latencies, P1 wave widths, and children's ages. Keywords. local polynomial smoothing, local cubic smoothing, kernel smoothing, corticla auditory evoked potential, CAEP, P1, P1 latency, wave width, cochlear implant
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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 | 0.008 |
| 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) | 0.009 | 0.003 |
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".