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Record W1997421167 · doi:10.1016/j.pain.2006.12.009

Pain among the oldest old in community and institutional settings

2007· article· en· W1997421167 on OpenAlexafffundabout
Jolanta Życzkowska, Katarzyna Szczerbińska, Micaela Jantzi, John P. Hirdes

Bibliographic record

VenuePain · 2007
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsUniversity of GuelphUniversity of Waterloo
FundersCanadian Institutes of Health Research
KeywordsMedicine

Abstract

fetched live from OpenAlex

The relationship between pain and increasing age was investigated using data from two different care settings collected on a province-wide basis in Ontario. Home care clients (HC) and complex continuing care patients (CCC) are assessed using the Resident Assessment Instrument-Home Care and Resident Assessment Instrument 2.0 instruments, respectively, as part of normal clinical practice. For this study, the sample was restricted to those aged 65 years and older and totaled 193,158 individuals. Centenarians (those 100 years of age or older) made up 0.41% (n=788) of the sample. Pain was assessed according to a previously validated pain scale embedded in both assessments that uses items on frequency and intensity. Based on 5-year age groups beginning at 65, the mean reported pain score was lower with each increment in age for men and women. Multiple logistic regression models were constructed and the odds ratios for pain in both HC and CCC groups decreased consistently in higher age groups after adjusting for disease diagnoses, cognition, functional status and health indicators. A model that included categories of analgesic medications coded based on the WHO pain ladder showed the relationship persisted after controlling for analgesia. In clinical settings, the oldest old appear to have lower levels of pain compared with the young old after adjusting for a variety of potential confounding variables.

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)0.025
metaresearch head score (Gemma)0.002
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 score0.025
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.255
Teacher spread0.241 · 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

Citations67
Published2007
Admission routes3
Has abstractyes

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