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Record W1988111734 · doi:10.3109/02699052.2014.976593

Traumatic brain injury in the elderly: A level 1 trauma centre study

2015· article· en· W1988111734 on OpenAlexafffundabout
Élaine de Guise, Joanne LeBlanc, Jehane H. Dagher, Simon Tinawi, Julie Lamoureux, Judith Marcoux, Mohammed Maleki, Mitra Feyz

Bibliographic record

VenueBrain Injury · 2015
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversité de MontréalMcGill University Health Centre
FundersMcGill University Health Centre
KeywordsTraumatic brain injuryPoison controlInjury preventionMedicineOccupational safety and healthSuicide preventionHuman factors and ergonomicsPsychologyMedical emergencyPhysical medicine and rehabilitationPsychiatryPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the characteristics and outcome of patients with TBI over 65 years old admitted to an acute care Level 1 Trauma centre in Montreal, Canada. METHODS: Data were retrospectively collected on patients (n = 1812) who were admitted post-TBI to the McGill University Health Centre-Montreal General Hospital from 2000-2011. The cohort was composed of four groups over 65 years old (65-75; 76-85; 86-95; and 96 and more). Outcome measures used were the extended Glasgow Outcome Scale (GOSE) as well as discharge destination. RESULTS: As the patients got older, the odds of having a poor outcome increased (OR = 2.344 for those 75-85 years old, 4.313 for those 86-95 years of age and 3.465 for those aged 96 years of age or older). Also, the proportion of patients going home or going home with out-patient rehabilitation decreased as age increased (p = 0.001 and p < 0.001, respectively). In contrast, the proportion of patients being discharged to long-term care facilities increased significantly as age increased (p < 0.001). CONCLUSION: This descriptive study provides a better understanding of characteristics and outcome of different age groups of patients with TBI all over 65 years old in Montreal, Canada.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 science0.0010.000
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 head0.095
GPT teacher head0.333
Teacher spread0.238 · 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

Citations22
Published2015
Admission routes3
Has abstractyes

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