Incidental lymphopenia and mortality: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: It is unknown if incidental lymphopenia detected in the general population is associated with higher all-cause and cause-specific mortality. We aimed to identify the associations between lymphopenia and all-cause and cause specific mortality. METHODS: In a prospective cohort study, we examined and followed participants enrolled in the Copenhagen General Population Study between November 2003 and April 2015. In our analysis, we modelled risks using Cox proportional hazards regression for 3 groups: participants with a lymphocyte count below the 2.5th percentile; those with a lymphocyte count at or between the 2.5th and 97.5th percentiles (reference category); and those with a lymphocyte count above the 97.5th percentile. RESULTS: The cohort included 108 135 participants with a median age of 68 years. During a median follow-up of 9 (interquartile range [IQR] 0–14) years, 10 372 participants died. We found that participants with lymphopenia (lymphocyte count < 1.1 × 109/L) compared with those with a lymphocyte count in the reference range (1.1–3.7 × 109/L) had higher mortality with multivariable adjusted hazard ratios (HRs) of 1.63 (95% confidence interval [CI] 1.51–1.76) for all causes, 1.67 (95% CI 1.42–1.97) for nonhematologic cancers, 2.79 (95% CI 1.82–4.28) for hematologic cancers, 1.88 (95% CI 1.61–2.20) for cardiovascular diseases, 1.88 (95% CI 1.55–2.29) for respiratory diseases, 1.86 (95% CI 1.53–2.25) for infectious diseases, and 1.50 (95% CI 1.19–1.88) for other causes. For all-cause mortality, the highest absolute 2-year risks of death were observed in women (61%) and men (75%) who smoked and were aged 80 years or older with lymphocyte counts less than 0.5 × 109/L. Participants with a lymphocyte count higher than the reference category had increased mortality (adjusted HR 1.17, 95% CI 1.04–1.31). INTERPRETATION: We found that lymphopenia was associated with an increased risk of all-cause and cause-specific mortality.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".