Impact of diabetes mellitus on pneumonia mortality in a senior population: results from the NHANES III follow-up study.
Bibliographic record
Abstract
OBJECTIVE: To examine whether diabetes mellitus increases the risk of pneumonia mortality among seniors in the U.S. general population. METHODS & RESULTS: The NHANES III follow-up study data were used. After excluding individuals from other minorities, being hospitalized with pneumonia in the previous year at baseline, or death of pneumonia during the first year of follow-up, a total of 3,707 subjects aged 65 years or older (1,794 men and 1,913 women) who had no missing information on variables for the analysis were included. Approximately 16% of seniors at baseline were diabetics, which was defined as either having been diagnosed by a physician, currently taking pills/insulin lowering blood glucose, or HbA1c higher than 6.4%. During an average 11 years of follow-up, a total of 98 deaths due to pneumonia were recorded (ICD-10: J12-J18). Cox-regression models were used to estimate the risk association between pneumonia mortality and diabetes mellitus. After adjustment for the covariates at baseline, the hazard ratios of pneumonia death were 1.30 (95% CI: 0.64-2.70) for pre-diabetics and 2.28 (95% CI: 1.18-4.39) for diabetics, respectively. Among those covariates, only age (HR (95% CI); 1.16 (1.13-1.20)), gender as female (0.35 (0.22-0.61)) and physical fitness measured as having no problem walking 1(+) mile during the previous month (0.38 (0.20-0.67)) reached statistical significance. CONCLUSIONS: The results suggest that diabetes mellitus is a strong risk predictor of pneumonia mortality and the evaluation of physical fitness may also be useful in the risk prediction of pneumonia mortality for seniors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".