Reported long-term effects of COVID-19 patients after hospital discharge in Jordan
Bibliographic record
Abstract
Coronavirus Disease 2019 (COVID-19) long-term effect is the new challenge facing healthcare providers that should be further assessed. We aim to describe the characteristics and patterns of long-term consequences of COVID-19 among recovered patients. COVID-19 patients baseline data was extracted from hospital records and alive patients filled self-reported symptoms questionnaires. A follow-up chest X-ray (CXR) was then scored based on lung abnormalities and compared with baseline CXR images. Six hundred ninety-four patients were included for the questionnaire and final analysis. Patients who were categorized as critical or severe were more prone to develop at least one symptom than those who were categorized as moderate. The most newly diagnosed comorbidities after discharge were diabetes (40.9%), cardiovascular diseases (18.6%), and hypertension (11.9%). Most patients with prolonged symptoms after discharge had a significant decrease in the quality of life. Small number of CXR showed persistent abnormalities in the middle right, the lower right, and lower left zones with an average overall score during admission 13.8 ± 4.9 and 0.3 ± 1.01 for the follow-up images. Effects of COVID-19 were found to persist even after the end of the infection. This would add on to the disease burden and would foster better management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".