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Agreement Between Patient and Proxy Responses of Health‐Related Quality of Life After Hip Fracture

2005· article· en· W1557646746 on OpenAlexafffundabout
C Allyson Jones, David Feeny

Bibliographic record

VenueJournal of the American Geriatrics Society · 2005
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsInstitute of Health EconomicsUniversity of Alberta
FundersUniversity of Alberta
KeywordsMedicineProxy (statistics)Intraclass correlationHip fractureProspective cohort studyQuality of life (healthcare)Health related quality of lifePhysical therapyGerontologyInternal medicinePsychometricsClinical psychologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine agreement between patient and proxy respondents on health-related quality of life (HRQL) over time during the 6-month recovery after hip fracture. DESIGN: Prospective longitudinal cohort study. SETTING: A healthcare region serving Edmonton, Alberta, and the surrounding area. PARTICIPANTS: Two hundred forty-five patients aged 65 and older, were treated for hip fracture, and had Mini-Mental State Examination scores greater than 17; 245 family caregivers participated as proxy respondents. MEASUREMENTS: Primary outcome was HRQL (Health Utilities Mark 2 and Mark 3). Interviews were completed within 5 days after surgery and at 1, 3, and 6 months. Agreement was evaluated using intraclass correlation coefficients (ICCs). RESULTS: Agreement was considered moderate to excellent for HRQL. ICC values ranged from 0.50 to 0.85 (P<.001) for physically based observable dimensions of health status and from 0.32 to 0.66 (P<.01) for less-observable dimensions. Agreement improved with time. Time and the number of days between patient and proxy interviews were significant factors in accounting for patient-proxy differences. CONCLUSION: Although proxy and patient responses are not interchangeable, proxy responses provide an option for assessing function and health status in patients who are unable to respond on their own behalf.

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 score0.059
Threshold uncertainty score0.334

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 head0.021
GPT teacher head0.312
Teacher spread0.292 · 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

Citations28
Published2005
Admission routes3
Has abstractyes

Explore more

Same venueJournal of the American Geriatrics SocietySame topicHip and Femur FracturesFrench-language works237,207