Gap-Prepulse Inhibition of the Acoustic Startle Reflex (GPIAS) for Tinnitus Assessment: Current Status and Future Directions
Bibliographic record
Abstract
The progress in the field of tinnitus largely depends on the development of a reliable tinnitus animal model. Recently, a new method based on the acoustic startle reflex modification was introduced for tinnitus screening in laboratory animals. This method was enthusiastically adopted and now widely used by many scientists in the field due to its seeming simplicity and a number of advantages over the other methods of tinnitus assessment. Furthermore, this method opened an opportunity for tinnitus assessment in humans as well. Unfortunately, multiple modifications of data collection and interpretation implemented in different labs make comparisons across studies very difficult. In addition, recent animal and human studies have challenged the original "filling-in" interpretation of the paradigm. Here, we review the current literature to emphasize on the commonalities and differences in data collection and interpretation across laboratories that are using this method for tinnitus assessment. We also propose future research directions that could be taken in order to establish whether or not this method is warranted as an indicator of the presence of tinnitus.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".