El síndrome clínico del post-COVID: ¿cuán profundo es el daño?
Bibliographic record
Abstract
Objectives: The objective of this research was to explore the initial and delayed symptoms after SARSCoV-2 infection, describe its impact and quantify the residual damage. Materials and methods: from July 1, 2020 to September 1, 2022, patients who had presented a positive result in a test for COVID-19 or a diagnosis under epidemiological criteria were included in an outpatient care center in the city of La Plata, evaluating risk factors, symptoms of their disease, persistent symptoms and findings in auxiliary cardiovascular, respiratory and neurocognitive tests. Results: 2252 patients with an average age of 42.21 years (SD: 14.17, range: 17-89) were evaluated. Patients with post-COVID-19 clinical syndrome were 1090 (48.4%), while 1162 (51.6%) had no symptoms. Among the post-COVID-19 symptoms, fatigue, dyspnea and cough were the most frequent, described in 28.2%, 21.8% and 14.4% respectively; while memory failure and concentration failure were reported in 10.2% of the patients questioned. An evaluation of the respiratory, cardiovascular and neurological test performance impact (Montreal Test, PHQ-9 and GAD-7) was also carried out. Patients with persistent symptoms were predominantly female, with an older average age and a higher smoking rate. Conclusions: no significant differences were observed in the cardiovascular or respiratory aspects between those who presented post-COVID-19 syndrome and those who did not. There was also no worse performance in neurocognitive tests when assessing post-COVID-19 symptoms compared to those who did not suffer from it; although when performing a subgroup analysis adjusted for age, those over 50 years of age showed worse performance.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".