Assessment of Long-Term Physical Function in Acute Respiratory Distress Syndrome (ARDS) Patients
Bibliographic record
Abstract
OBJECTIVE: It is often important to adjust for the effect of comorbid diseases on patient outcomes. This study compares the association between physical function in acute respiratory distress syndrome patients with scores on two comorbidity indices, the Charlson Comorbidity Index, designed to predict mortality, and the Functional Comorbidity Index (FCI), which was designed to predict physical function. DESIGN: This is a prospective, longitudinal, observational study. A total of 73 survivors of acute respiratory distress syndrome were contacted at 3, 6, and 12 mos. Patient comorbidity was evaluated with the Charlson Comorbidity Index and the FCI. Physical function was measured using the Medical Outcomes Study 36-Item Short Form Health Survey Physical Function Subscale and the Physical Component Subscale scores. RESULT: Mean FCI and Charlson Comorbidity Index scores correlated fairly strongly (Spearman rho = 0.62, P < 0.001). FCI, but not the Charlson Comorbidity Index, scores correlated with the Physical Function Subscale and Physical Component Subscale scores. After controlling for other potentially confounding variables such as age and severity of illness through regression analysis, the FCI score was still significantly associated with both Physical Function Subscale and Physical Component Subscale scores. CONCLUSIONS: The FCI is a better method of measuring comorbidity with physical function as the outcome. This study illustrates the importance of choosing the most appropriate comorbidity index for the outcome of interest.
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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 | 0.000 |
| 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 | 0.001 |
| 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".