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Record W3204519328 · doi:10.1111/ajag.13000

Injury profiles and clinical management of older patients with major trauma

2021· article· en· W3204519328 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAustralasian Journal on Ageing · 2021
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicinePsychological interventionInjury Severity ScoreMajor traumaHead injuryHead traumaInjury preventionPhysical therapyPoison controlEmergency medicineSurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: This study aimed to characterise the most common injury profiles and interventions in older major trauma patients, and how they change with age. METHODS: This is a retrospective review of interventions, injury profiles and outcomes of major trauma patients aged 65 years and older from 2007 to 2018, using data from the Victorian State Trauma Registry. A latent class analysis (LCA) was used to identify homogenous injury groups. RESULTS: The LCA identified five injury profiles: isolated head injury; chest/upper limb injuries; multi-trauma; isolated spine; and head/chest/upper limb. Among 10,001 patients, 50% had an isolated head injury, and 83% of patients received definitive treatment at a major trauma centre. 50% of patients received a surgical or non-surgical intervention, and 36% underwent surgery. These proportions declined with increasing age. CONCLUSIONS: Older patients with major trauma are a heterogeneous group, whose mechanisms and patterns of injury, and clinical management change with increasing age.

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.

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)0.000
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 score0.245
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.022
GPT teacher head0.317
Teacher spread0.295 · 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