Persistent olfactory dysfunction associated with poor sleep quality and anxiety in patients with long COVID-19
Bibliographic record
Abstract
Introduction: Long COVID-19 is a multisystemic condition characterized by signs and symptoms persisting for more than 4 weeks after the acute infection. Poor sleep quality has been widely reported in patients with Long COVID-19. Determining the characteristics, type, severity, and relationship of this condition with other neurological symptoms is essential for the prognosis and proper management of sleep disturbances. Objective: The aim of this study was to describe the sociodemographic and clinical characteristics of patients with Long COVID-19 who present with poor sleep quality and to investigate its relationship with other neurological symptoms, such as anxiety, cognitive impairment, and olfactory dysfunction. Methods: A cross-sectional study was conducted between November 2020 and October 2022, involving 288 patients with Long COVID-19 and self-reported neurological symptoms. Patients were assessed using standardized protocols: Pittsburgh Sleep Quality Index (PSQI), Beck Anxiety Inventory (BAI), Clinical Research Center for Chemosensory (CCRC) evaluation, and Montreal Cognitive Assessment (MoCA). Results: The study showed that 27% of patients had poor sleep quality, particularly women and younger individuals with complaints of anxiety and olfactory dysfunction. Previous and recent neuroimaging studies suggest a significant link between poor sleep quality and psychological disorders over time, in addition to functional and structural changes in patients with persistent olfactory dysfunction. Poor sleep quality is an essential component of the complex neurological changes observed in Long COVID-19, and its inclusion in the clinical management of these patients is recommended. Conclusion: A high prevalence of poor sleep quality and associated neurological symptoms, such as anxiety and olfactory dysfunction, was observed in patients with Long COVID-19. These findings reinforce the importance of continuous monitoring of these symptoms to prevent potential worsening in this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".