Bibliographic record
Abstract
We all appreciate that sensorineural hearing loss(SNHL)can be caused by numerous factors.However, for clinical purposes this hearing problem is most often distilled down and characterized by a simple threshold audiogram with perhaps a few other diagnostic tests.In reality, the SNHL "basket" is filled with dozens of separable types of hearing loss depending on etiology, on the anatomical location of the pathology within the cochlea, or on some special characteristic symptoms.Over the past several decades, we have derived a wealth of knowledge about different sub-types of SNHL from animal models.In these studies the cause of a hearing loss can be controlled, and anatomical and biological changes that result in auditory dysfunction can be closely examined.However, we can ask : "has this new knowledge significantly changed clinical practice?"Do we still simply look at an audiogram and describe the "shape" of the threshold curve as if it is the defining feature of the hearing loss?We are long overdue for some other system(s)for defining specific types of SNHL.
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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.017 |
| Insufficient payload (model declined to judge) | 0.054 | 0.052 |
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".