Frailty Hinders Recovery From Influenza and Acute Respiratory Illness in Older Adults
Bibliographic record
Abstract
BACKGROUND: We examined frailty as a predictor of recovery in older adults hospitalized with influenza and acute respiratory illness. METHODS: A total of 5011 patients aged ≥65 years were admitted to Canadian Serious Outcomes Surveillance Network hospitals during the 2011/2012, 2012/2013, and 2013/2014 influenza seasons. Frailty was measured using a previously validated frailty index (FI). Poor recovery was defined as death by 30 days postdischarge or an increase of more than 0.06 (≥2 persistent new health deficits) on the FI. Multivariable logistic regression controlled for age, sex, season, influenza diagnosis, and influenza vaccination status. RESULTS: Mean age was 79.4 (standard deviation = 8.4) years; 53.1% were women. At baseline, 15.0% (n = 750) were nonfrail, 39.3% (n = 1971) were prefrail, 39.8% (n = 1995) were frail, and 5.9% (n = 295) were most frail. Poor recovery was experienced by 21.4%, 52.0% of whom had died. Frailty was associated with lower odds of recovery in all 3 seasons: 2011/2012 (odds ratio [OR] = 0.70; 95% confidence interval [CI], 0.59-0.84), 2012/2013 (OR = 0.72; 95% CI, 0.66-0.79), and 2013/2014 (OR = 0.75; 95% CI, 0.69-0.82); results varied by season, influenza status, vaccination status, and age. CONCLUSIONS: Increasing frailty is associated with lower odds of recovery, and persistent worsening frailty is an important adverse outcome of acute illness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".