Bibliographic record
Abstract
【Purpose: To investigate the post-intensive care syndrome (PICS) and to analyze the factors affecting the quality of life (QoL) of survivors of critical illness. Methods: Subjects were 114 outpatients who had been discharged from intensive care units of a university hospital in B city, Korea. From July 30 through September 30, 2015, PICS was assessed using the Korean Montreal Cognitive Assessment, Hospital Anxiety-Depression Scale, Korean Instrumental/Activities of Daily Living (K-I/ADL) index, and handwriting transformation, while physical and mental health-related QoL was measured using the SF-12. Results: Of the subjects, 39.5% were screened for mild cognitive disorder and 23.7% experienced handwriting transformation after discharge. Multiple regression analysis revealed that restraint application, current job, time of ${\geq}36$ months after discharge, depression, anxiety, and handwriting transformation accounted for 40.9% of the physical health-related QoL, and depression, anxiety and experience of delirium accounted for 62.4% of the mental health-related QoL. Conclusions: It is necessary to make efforts to reduce restraint application in intensive care units and prevent the occurrence of delirium, with the objective of reducing PICS and improving the QoL of critical illness survivors.】
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".