Follow-up study about post-traumatic stress disorder and cognition in patients transferred from ICU
Bibliographic record
Abstract
Objective To explore the dynamic change and relationship between post-traumatic stress disorder (PTSD) and cognition in patients transferred from ICU. Methods Participants patients were selected from Critical Medical Department of the First Affiliated Hospital of Xi’an Jiaotong University by convenience sampling method between October 2016 to February 2017. The Montreal Cognitive Assessment (MoCA) and the PTSD Cheeklist-Vivilian Version (PCL-C) were used to collect data at 3 days, 3 months and 6 months after transferring from ICU. Results The incidence of cognitive disorder at 3 time points were 29.4% (30/102) , 20.0% (18/90) , 17.8% (13/73) respectively, and MoCA scores was 25.83±6.29,28.57±5.43,28.86±5.11, the difference was significant (F=6.204, P<0.01). The incidence of PTSD symptoms were 42.2% (43/102) , 23.3% (21/90) , 19.2% (14/73) respectively, and PCL-C scores was 35.24±5.94, 28.68±5.13, 26.92±4.85, the difference was significant (F=10.125, P<0.01). There were significant relationship between cognition and PTSD level (r=0.299-0.543, P <0.05). The PTSD level in cognitive disorder patients was 37.52±5.88, 31.15±5.12, 29.84±4.82, and that in non-cognitive disorder patients was 34.32±5.76, 27.68±4.91, 25.74±4.59 the difference was significant (t=2.117, 2.651, 3.334, P<0.05). Conclusions Health workers should pay attention to the mental status and cognitive impairment of patients transferred from ICU, implement psychological and cognitive interventions early, which could improve the cognitive status and PTSD progression of patients, and improve their quality of life. Key words: Patients transferred from ICU; Post-traumatic stress disorder; Cognition; Follow-up study
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".