Frequency-Following Response (FFR) em usuários de implante coclear: uma revisão sistemática dos parâmetros de aquisição, análise e resultados
Bibliographic record
Abstract
PURPOSE: To characterize the acquisition parameters, analysis, and results of the frequency-following response (FFR) in cochlear implant users. RESEARCH STRATEGIES: The search was conducted in Cochrane Library, Latin American and Caribbean Health Sciences Literature (LILACS), Ovid Technologies, PubMed, SciELO, ScienceDirect, Scopus, Web of Science, and gray literature. SELECTION CRITERIA: Studies on FFR in cochlear implant users or that compared them with normal-hearing people, with no restriction of age, were included. Secondary and experimental studies were excluded. There was no restriction of language or year of publication. DATA ANALYSIS: The data were analyzed and reported according to the stages in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), 2020. The methodological quality was analyzed with the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. Divergences were solved by a third researcher. RESULTS: Six studies met the inclusion criteria. Only one study was comparative, whose control group comprised normal-hearing people. The variations in acquisition parameters were common and the analysis predominantly approached the time domain. Cochlear implant users had different FFR results from those of normal-hearing people, considering the existing literature. Most articles had low methodological quality. CONCLUSION: There is no standardized FFR acquisition and analysis protocol for cochlear implant users. The results have a high risk of bias.
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 | 0.093 | 0.235 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".