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Record W3174814956 · doi:10.1177/07334648211024790

Last Year of Life, Frailty, and Out-of-Pocket Expenses in Older Adults: A Secondary Analysis of the Mexican Health and Aging Study

2021· article· en· W3174814956 on OpenAlexaff
Guillermo Salinas‐Escudero, María Fernanda Carrillo-Vega, Carmen García‐Peña, Silvia Martínez‐Valverde, Luis David Jácome-Maldonado, Matteo Cesari, Mario Ulises Pérez‐Zepeda

Bibliographic record

VenueJournal of Applied Gerontology · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsNova Scotia Health AuthorityDalhousie University
FundersNational Institute on AgingSecretaría de Ciencia, Tecnología e Innovación del Distrito Federal
KeywordsMedicineGerontologyDemographyProbit modelCross-sectional studyCohortStatistics

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the association of frailty with out-of-pocket expenses (OOPEs) during the last year of life of Mexican older adults. METHODS: Cross-sectional secondary analysis of the Mexican Health and Aging Study (MHAS), a representative population-based cohort study. Health care expenses were estimated, and a probit model was used to estimate the probability that older adults had OOPE. A general linear model was applied to explain OOPE magnitudes. RESULTS: A total of 55.8% of individuals reported having OOPE with a mean of 3,261 USD. Average OOPE for hospitalization during the last year of life was 7,011.9 USD. Older adults taking their own medical decisions during the last year of life expended less than those who did not. CONCLUSION: No affiliation to health services, frailty, and health decision-making by others increased the probability of OOPE. The magnitude is determined by age, hospitalization, medical visits, affiliation, frailty, and health decision-making by others.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.040
GPT teacher head0.287
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2021
Admission routes1
Has abstractyes

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