Post-traumatic stress among COVID-19 survivors: A descriptive study of hospitalized first-wave survivors
Bibliographic record
Abstract
Introduction: The coronavirus Severe Acute Respiratory Syndrome Coronavirus Type 1 induces a severe respiratory disease, coronavirus disease 2019 (COVID-19). After Severe Acute Respiratory Syndrome Coronavirus Type 1 and Middle East Respiratory Syndrome infection, increased post-traumatic stress disorder (PTSD) rates were described. Methods: This single-centred, prospective study aimed to evaluate the rates of PTSD in patients who were hospitalized for COVID-19. Inclusion criteria were COVID-19 patients hospitalized in the intensive care unit (ICU) or in a standard unit with at least 2 L/min oxygen. Six months post-hospitalization, subjects were assessed for PTSD using a validated screening tool, the Post-Traumatic Stress Checklist-5 (PCL-5). Results: A total of 40 patients were included. No demographic differences between the ICU and non-ICU groups were found. The mean PCL-5 score for the population was 8.85±10. The mean PCL-5 score was 6.7±8 in the ICU group and 10.5±11 in the non-ICU group (P=0.27). We screened one patient with a positive PCL-5 score and one with a possible PCL-5 cluster score. Nine patients had a PCL-5 score of up to 15. Seven patients reported no symptoms. Seven patients accepted a psychological follow-up: one for PTSD, three for possible PTSD and three for other psychological problems. Discussion: The PCL-5 tool can be used by lung physicians during consultations to identify patients for whom follow-up mental health assessment and treatment for PTSD are warranted. Conclusion: Lung physicians should be aware of the risk of PTSD in patients hospitalized for COVID-19 and ensure appropriate screening and follow-up care.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".