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Record W6997144924

Traumatic Brain Injury in Older Adults: A Descriptive & Etiologic Analysis

2016· dissertation· en· W6997144924 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2016
Typedissertation
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsLogistic regressionDepression (economics)Incidence (geometry)Traumatic brain injuryConfoundingPoison controlOdds ratioInjury prevention
DOInot available

Abstract

fetched live from OpenAlex

A two-part study was undertaken to determine the characteristics and incidence of older adults
\nwho sustained a traumatic brain injury (TBI) while in Ontario home care from 2003 to 2013, and
\nto determine the association between depression and sustaining a TBI. Both parts used data from
\nthe Ontario Association of Community Care Access Center?s database. Data were retrieved for
\nall service users 65 years or older who had home care between 2003 and 2013; these data are
\nbased on the Resident Assessment Instrument-Home Care. The variables used in the analyses
\nincluded: TBI, depression, demographics, neurological conditions and history of falling. For the
\ndescriptive component, comparisons of characteristics were made between service users who did
\nand did not sustain a TBI using odds ratios (OR). The ten-year trend of annual cumulative
\nincidence and standardized incidence rates were assessed using regression. For the etiologic
\ncomponent, incident TBI cases were matched to four controls by age, sex and date of assessment.
\nCrude OR?s were determined for the association between depression and TBI. Multivariable
\nconditional logistic regression was used to adjust for potential confounders and identify effect
\nmodifiers. Multivariable estimates were stratified by history of falling. A total of 554,313 service
\nusers were included, of which 5215 (0.9%) had a TBI and 39,048 (7.0%) had depression.
\nCharacteristics associated with TBI were: male sex (OR: 1.54, 95% CI: 1.45, 1.62), aboriginal
\norigin (OR: 1.98; 95% CI: 1.57, 2.50), increasing age (OR: 1.22, 95% CI: 1.09, 1.35 for 70-74;
\nup to OR: 2.31, 95% CI: 2.05, 2.59 for >90; referent group 65-69), being widowed (OR: 1.59,
\n95% CI: 1.41, 1.80), having a history of one or more falls (OR: 2.31, 95% CI: 2.19, 2.44), the
\nuse of antidepressants (OR: 1.49, 95% CI: 1.40, 1.59) and the presence of depression (OR: 1.57,
\n95% CI: 1.43, 1.71), dementia (OR: 1.65, 95% CI: 1.54, 1.76), hemiplegia (OR: 4.34, 95% CI:
\n3.88, 4.85), multiple sclerosis (OR: 3.19, 95% CI: 2.49, 4.08) and parkinsonism (OR: 1.22, 95% CI: 1.07, 1.38). Incidence was significantly higher than previously reported figures in the general
\npopulation. There was a decrease in the annual cumulative incidence over the ten-year period.
\nFemale standardized rates decreased significantly (p<0.05) in a linear fashion while male and
\noverall decreased in a non-linear fashion. The crude OR for the association between depression
\nand TBI was 1.54 (95% CI: 1.43, 1.64). Stratified analyses indicated that the association was
\nsignificantly different for those with a history of falling (OR: 1.45, 95% CI: 1.22, 1.73) and those
\nwithout a history of falling (OR: 1.19, 95% CI: 0.99, 1.42). Multivariable analysis suggested that
\nthere were three significant effect modifiers for the exposure: history of falling, level of
\neducation and Alzheimer?s. As the level of education increased, the association between
\ndepression and TBI became smaller (OR: 1.88, 95% CI: 1.30, 2.70 for 8th grade or less compared
\nto OR: 1.11, 95% CI: 0.78, 1.65 for graduate degree). Service users with a TBI had greater odds
\nof having a history of falling (OR: 1.45, 95% CI: 1.22, 1.73) and being diagnosed with
\nAlzheimer?s Disease (OR: 1.18, 95% CI: 1.05, 1.32). Longitudinal studies are needed to confirm
\nthis finding, as our study was cross-sectional in nature, and to investigate the association between
\nother chronic conditions and TBI.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.067
GPT teacher head0.331
Teacher spread0.264 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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