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Record W2749700975 · doi:10.2105/ajph.2017.303932

Standard Period Life Table Used to Compute the Life Expectancy of Diseased Subpopulations: More Confusing Than Helpful

2017· article· en· W2749700975 on OpenAlexaff
Linda Perron, Marc Simard, Jacques Brisson, Denis Hamel, Ernest Lo

Bibliographic record

VenueAmerican Journal of Public Health · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsLife expectancyCohortLife tableMetric (unit)MedicineDemographyPopulationGerontologyStatisticsMathematicsInternal medicineOperations managementEnvironmental health

Abstract

fetched live from OpenAlex

Life expectancy (LE) based on a period life table (PLT) traditionally serves as a general population summary metric. It is, however, becoming more frequently reported for chronically afflicted subpopulations. In general populations, there is always an obvious real cohort sharing the hypothetical PLT cohort characteristics, and the LE estimate is intuitively understood as that real cohort mean survival time, assuming constancy of death risks. In diseased subpopulations, the correspondence between the hypothetical cohort and a real cohort is not straightforward. Furthermore, the excess mortality of chronic diseases usually changes according to age at onset and time since onset. The standard PLT method does not allow for proper control of these issues, so the LE estimate can only be deemed valid under specific assumptions. Without clear statements about the real cohort to whom the estimate is intended and the assumptions allowing disregard of the effect of age at onset and time since onset, LEs of afflicted subpopulations computed with the PLT are only abstract numbers summarizing mortality rates. If called "life expectancy," they can be seriously misleading. The same applies to health-adjusted LE.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.396
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2017
Admission routes1
Has abstractyes

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