Bipolar spectrum, hypothyroidism, and their association with chronic fatigue/myalgic encephalomyelitis-like syndrome in long COVID: could they be identified as early determinants?
Bibliographic record
Abstract
Background: Long COVID has been increasingly linked to persistent clinical manifestations, including chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). However, the relationship between this syndrome and pre-existing conditions such as bipolar spectrum disorders and hypothyroidism is not yet clearly established. These disorders may influence the regulation of biorhythms and immune function, suggesting a possible role in the predisposition to the development of CFS/ME in the context of long-term COVID-19. Objectives: This study investigates the prevalence of hypothyroidism and bipolar spectrum disorders in patients with CFS/ME associated with long-term COVID-19. It compares it with pre-pandemic population data to determine whether these conditions may be predisposing factors. Methods: A case-control design was used to select cases from a clinical trial on CFS/ME in long COVID, while controls were extracted from pre-COVID epidemiological databases. Comparative statistical analyses, including chi-square tests and analysis of variance (ANOVA), were performed to assess significant differences in the frequency of these conditions between both groups. Results: < 0.0001 for both). Similarly, individuals screening positive for depressive symptoms (PHQ9 > 9) showed markedly increased odds (55.5% vs. 4.16%; OR = 28.75; 95% CI: 6.52-126.73). Conclusion: The findings suggest that hypothyroidism and bipolar spectrum disorders may act as predisposing factors in the development of CFS/ME in long-term COVID-19. Identifying these clinical antecedents could facilitate early detection and the development of targeted intervention strategies in at-risk populations.
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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".