Comorbidities Might Condition the Recovery of Quality of Life in Survivors of Sepsis
Bibliographic record
Abstract
PURPOSE:: To assess how preexisting disabling comorbidities (DC) affect the recovery rate of quality of life (QOL) over time in sepsis survivors. METHODS:: A prospective study was conducted on sepsis survivors who answered the 36-Item Short Form Health Survey (SF-36) 7 days after discharge from the intensive care unit. Subsequent interviews were held at 3, 6, and 12 months. The results of the physical component score (PCS) and mental component score (MCS) of the SF-36 were evaluated. Patients were divided into 2 groups to compare patients with DC (DC group) and without DC (no-DC group). Quantile regression was used to model changes in PCS and MCS between different time points. RESULTS:: Seventy-nine sepsis survivors were enrolled. After controlling for baseline age and QOL, the QOL scores were lower among patients with DC than in no-DC patients. The QOL of DC group got worse when compared to no-DC group. Recovery rate of PCS and MCS was higher in the DC group than in the no-DC group (PCS: 20.51 vs 16.96, P < .01; MCS: 19.24 vs 9.66, P < .01). Their baseline QOL was recovered only by 6 months after the sepsis episode. CONCLUSION:: Quality-of-life impairment and its recovery rhythm in patients with sepsis appear to be conditioned by coexisting DC.
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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 | 0.003 |
| 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".