Speech intelligibility in adults after the new coronavirus infection (COVID-19)
Bibliographic record
Abstract
OBJECTIVE: The research was aimed to assess speech intelligibility in adults after the new coronavirus infection (COVID-19), state of peripheral and central parts of auditory system and cognitive functions. MATERIAL AND METHODS: 26 people complaining about hearing loss, poor speech intelligibility and/or tinnitus after COVID-19 were examined. All the patients underwent the basic audiological assessment before COVID-19. Extended testing in patients after COVID-19 included: pure tone audiometry, impedancemetry, speech audiometry in quiet and noise (evaluation of monosyllabic words intelligibility and the Russian matrix sentence test RuMatrix), the alternating binaural speech test, the dichotic digits test and the Montreal Cognitive Assessment (MoCA). RESULTS: The most significant deviations from the normative values were obtained in the RuMatrix test and the dichotic digits test that may be due to both central auditory processing disorder and memory impairment. Low MoCA scores were obtained in 62% of patients. CONCLUSION: Deterioration of speech intelligibility after COVID-19 was revealed, both in patients with hearing loss and with normal hearing that corresponded to their complaints. It may be caused by central auditory disorder, memory impairment or cognitive status lesion. The correlation found between the results of the RuMatrix test in noise and the severity of the COVID-19 may indicate the impact of the virus on the auditory cortex.
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 | 0.000 | 0.001 |
| 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".