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Record W2049652697 · doi:10.1186/1471-2318-10-1

Predictors of health decline in older adults with pneumonia: findings from the Community Acquired Pneumonia Impact Study

2010· article· en· W2049652697 on OpenAlexafffundabout
E. Fernández, Paul Krueger, Mark Loeb

Bibliographic record

VenueBMC Geriatrics · 2010
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of TorontoMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMedicineGerontologyCommunity-acquired pneumoniaPsychological interventionPneumoniaCommunity healthQuality of life (healthcare)RehabilitationSocial supportFamily medicinePublic healthPhysical therapyPsychiatryNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to identify predictors of health decline among older adults with clinically diagnosed community acquired pneumonia (CAP). It was hypothesized that older adults with CAP who had lower levels of social support would be more likely to report a decline in health. METHODS: A telephone survey was used to collect detailed information from older adults about their experiences with CAP. A broader determinants of health framework was used to guide data collection. This was a community wide study with participants being recruited from all radiology clinics in one Ontario community. RESULTS: The most important predictors of a health decline included: two symptoms (no energy; diaphoresis), two lifestyle variables (being very active; allowing people to smoke in their home), one quality of life variable (little difficulty in doing usual daily activities) and one social support variable (having siblings). CONCLUSIONS: A multiplicity of factors was found to be associated with a decline in health among older adults with clinically diagnosed CAP. These findings may be useful to physicians, family caregivers and others for screening older adults and providing interventions to help ensure positive health outcomes.

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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.298
Teacher spread0.280 · 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

Citations34
Published2010
Admission routes3
Has abstractyes

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