Epidemiological and clinical profile of COVID-19 patients with psychiatric disorders admitted to Udayana University Hospital during the first year of the COVID-19 pandemic
Bibliographic record
Abstract
Background.Not only causing major implications on physical medicine, COVID-19 had changed the landscape in psychiatric medicine.The world is facing an impending surge of psychiatric disorders, and the early signs are now clearer than ever.These early signs might help psychiatrist and physicians, in general, to more accurately analyse, diagnose and treat these psychiatric disorders.Objectives.To report the epidemiological and clinical characteristics of COVID-19 patients who experience psychiatric symptoms. Material and methods.The data was collected by secondary data in the form of medical records from patients treated at Udayana University Hospital within the period April 2020 to March 2021. Results.Patients with psychiatric disorders admitted to this hospital (n = 94) had a mean age of 48.5 (SD ± 14.5) years of age, with males constituting 51.1%.The psychiatric diagnoses found were insomnia (44.7%), adjustment disorder (26.6%), anxiety disorder (16.0%), depression (6.4%), psychosis (4.3%), bipolar disorder (3.2%), as well as delirium, acute stress reaction and schizophrenia at 2.1% each.These patients had a mean duration of hospitalisation of 13.2 (SD ± 6.1) days.The hospital recorded a fatality rate of 7.4% in this particular element of patients, higher than the fatality rate observed in those of the whole population.Conclusions.The first year of the COVID-19 pandemic in Indonesia, though not directly implying, was a warning sign of the impending surge of the number of psychiatric diagnoses in the future.These psychiatric patients are not to be left alone and ignored, as they suggest a possible increase in fatality rate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".