Going beyond the disability-based morbidity definition in the compression of morbidity framework
Bibliographic record
Abstract
BACKGROUND: As originally proposed by Fries, conceptualizing morbidity solely through associated functional limitation/disability (FL/D) remains the most widely accepted metric to assess whether increases in longevity have been accompanied by a compression of morbidity. OBJECTIVE: To propose a departure from a highly restrictive FL/D-based definition of "morbidity" to a broader view that considers the burden of chronic diseases even when no overt FL/D occur. DESIGN: We outline three reasons why the current framework of compression of morbidity should be broadened to also consider morbidity to be present even when there are no overtly measurable FL/D. We discuss various scenarios of morbidity compression and morbidity expansion under this broader rubric of morbidity. CONCLUSION: The rationale to go beyond a purely FL/D-based definition of morbidity includes: (1) substantial damage from chronic disease that can develop prior to overt FL/D symptoms occurring; (2) multiple costs to the individual and society that extend beyond FL/D, including medication costs, health care visits, and opportunity costs of lifelong treatment; and (3) psychosocial and stress burden of being labeled as diseased and the consequence for overall well-being. Adopting this broader definition of morbidity suggests that increases in longevity have been possibly accompanied by an expansion of morbidity, in contrast to Fries' original hypothesis that morbidity onset (based on only FL/D) would be delayed to a greater extent than increases in survival. There is an urgent need for better data and more research to document morbidity onset and its link with increases in longevity and assess the important question on whether populations while living longer are also healthier.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".