Symptom Profile of Patients With Post-COVID-19 Conditions and Influencing Factors for Recovery
Bibliographic record
Abstract
Background: The aim of the study was to examine the factors that influence the improvement of post-coronavirus disease 2019 (COVID-19) symptoms. Methods: We investigated the biomarkers and post-COVID-19 symptoms status of 120 post-COVID-19 symptomatic outpatients (44 males and 76 females) visiting our hospital. This study was a retrospective analysis, so we analyzed the course of symptoms only for those who could follow the progress of the symptoms for 12 weeks. We analyzed the data including the intake of zinc acetate hydrate. Results: The main symptoms that remained after 12 weeks were, in descending order: taste disorder, olfactory disorder, hair loss, and fatigue. Fatigue was improved in all cases treated with zinc acetate hydrate 8 weeks later, exhibiting a significant difference from the untreated group (P = 0.030). The similar trend was observed even 12 weeks later, although there was no significant difference (P = 0.060). With respect to hair loss, the group treated with zinc acetate hydrate showed significant improvements 4, 8, and 12 weeks later, compared with the untreated group (P = 0.002, P = 0.002, and P = 0.006). Conclusion: Zinc acetate hydrate may improve fatigue and hair loss as symptoms after contracting COVID-19.
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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 | 0.000 | 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".