Report of a SPEAC webinar 22 september 2023: Sensorineural hearing loss, lassa virus disease and vaccines
Bibliographic record
Abstract
Lassa virus (LASV) belongs to the Arenavirus family. LASV is endemic in several West Africa countries and causes viral hemorrhagic fevers. The Nigeria CDC has reported that an outbreak in 2024 in 28 states has resulted in 7767 suspected cases of Lassa fever, 971 confirmed cases and 166 confirmed deaths up to 11 August. Since infection with LASV can result in sensorineural hearing loss (SNHL) in up to 30% of patients, there are questions about whether triggering the immune response by immunization with LASV vaccines could potentially cause SNHL, although this has not been shown in clinical trials to date. To address this issue, the Coalition for Epidemic Preparedness Innovations (CEPI) and the Brighton Collaboration (BC) Safety Platform for Emergency vACcines (SPEAC) convened a three-hour webinar on 22 September 2023 to review what is known from both animal studies and human clinical trials and how hearing assessments in future clinical trials can help to assess the risk. This report summarizes the evidence presented and provides considerations for hearing assessment in expanded human trials of LASV vaccine candidates in children and adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.012 |
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".