Discourse impairment and inflammatory markers in long covid
Bibliographic record
Abstract
Background: While meta-analyses report moderate language deficits in individuals with long COVID-19, current evidence is limited to simple language tasks (e.g., verbal fluency, naming). Discourse analysis offers a more ecologically valid approach, capturing complex and functionally relevant aspects of language. Objectives: This study aims to (1) characterize discourse impairments in long COVID and (2) examine their associations with peripheral inflammatory markers measured during both the acute phase and in long-COVID-19. Methods: We assessed 97 adults (mean age=56.6±13.7 years) previously hospitalized with RT-PCR-confirmed COVID-19 in Rio de Janeiro, Brazil, between May 2020 and March 2021. Neuropsychological and speech-language evaluations were conducted 3 to 8 months post-discharge (mean=168.5±90.3 days). Language was assessed using the Brief Montreal-Toulouse Language Assessment Battery and a narrative discourse task (“car accident” story). Blood samples collected during hospitalization and at follow-up were analyzed for inflammatory markers. Results: Discourse impairments were identified in 10.3% (conversational) and 15.5% (narrative) of participants. Notably, poorer discourse performance was associated with lower MCP-1 levels during the acute phase (p=0.042) and higher VEGF levels at follow-up (p=0.015). Discussion: Discourse impairments are present in a subset of individuals with long COVID and may be linked to dysregulated immune responses at acute phase and persistent endothelial dysfunction at follow-up. Conclusion: These findings underscore the relevance of ecologically valid assessments in post-COVID care and highlight neuroinflammation as a potential mechanism and therapeutic target.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".