Frailty and survival in the 1918 influenza pandemic
Bibliographic record
Abstract
One of the most well-known yet least understood aspects of the 1918 influenza pandemic is the disproportionately high mortality among young adults. Contemporary accounts further describe the victims as healthy young adults, which is contrary to the understanding of selective mortality, which posits that individuals with the highest frailty within a group are at the greatest risk of death. We use a bioarchaeological approach, combining individual-level information on health and stress gleaned from the skeletal remains of individuals who died in 1918 to determine whether healthy individuals were dying during the 1918 pandemic or whether underlying frailty contributed to an increased risk of mortality. Skeletal data on tibial periosteal new bone formation were obtained from 369 individuals from the Hamann-Todd documented osteological collection in Cleveland, Ohio. Skeletal data were analyzed alongside known age at death using Kaplan-Meier survival and Cox proportional hazards analysis. The results suggest that frail or unhealthy individuals were more likely to die during the pandemic than those who were not frail. During the flu, the estimated hazards for individuals with periosteal lesions that were active at the time of death were over two times higher compared to the control group. The results contradict prior assumptions about selective mortality during the 1918 influenza pandemic. Even among young adults, not everyone was equally likely to die-those with evidence of systemic stress suffered greater mortality. These findings provide time depth to our understanding of how variation in life experiences can impact morbidity and mortality even during a pandemic caused by a novel pathogen.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".