The effects of age of cochlear implantation on speech perception outcomes in prelingually deaf children
Bibliographic record
Abstract
We have made a retrospective analysis on 70 prelingually deaf children (78% congenital; age range 2-15 years) followed for up to 5 years post-implant during which both closed set speech perception tests (TAC, WIPI) and open set tasks (PBK, GASP) were administered. We used a binary partitioning algorithm to optimally divide our dataset on the basis of age at implantation This technique achieves an optimal split when the heterogeneity of the data is most reduced (maximal drop in deviance). For the closed set speech perception tests (TAC and WIPI) partitioning best divided-out data at age 4.4 years. For the open set tests optimal division was at a higher age of implantation (GASP word, 5.6 years; PBK word, 8.4 years). Using these partitioning values, we have found statistically significant differences between rate of improvement of scores in the younger implanted children compared with those implanted later.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".