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Record W2139187584

Injury patterns and outcomes associated with elderly trauma victims in Kingston, Ontario.

2007· article· en· W2139187584 on OpenAlexaffabout
Rob Gowing, Minto Jain

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsMedicineInjury Severity ScoreInjury preventionIntensive care unitRetrospective cohort studyPoison controlEmergency medicineTrauma centerPediatricsSurgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To characterize the common injuries incurred by elderly trauma victims and to identify the most frequent complications and outcomes. METHODS: We undertook a retrospective descriptive chart review of 125 consecutive patients who were over age 65 years and who were admitted to an academic hospital in Kingston, Ontario, over a 3-year period with an injury severity score (ISS) > 12. Complete data about the mechanism of injury (MOI), age, date, sex, specific injury, principal and secondary diagnoses, comorbid conditions, intensive care unit (ICU) and hospital length of stay and discharge disposition were recorded for 99 of these patients. RESULTS: Elderly trauma cases accounted for 125 of the total 460 trauma admissions over 3 years. For that same period, more than 50% of trauma deaths occurred among elderly patients, of whom 65 were men and 34 were women. Their mean age was 77 (standard deviation [SD] 6) years, with an age range of 66-95 years. The average ISS score was 23 (SD 13), with a range of 12-75. MOI included falls (64%), motor vehicle collision (27%), injury from machinery (3%), injury from natural and environmental causes (2%), suicide or self-inflicted injury (3%) and burns (1%). The mean length of stay was 14.6 days, but this ranged from 1 to 111 days. Of the 99 patients, 14 were admitted to the ICU for a total of 37 days, and 9 of these died. Of the total of 67 (67%) patients who were discharged from hospital, 46% were discharged home and 32% died. Falls accounted for the most frequent MOI, followed by motor vehicle collisions. The most common injury in the falls group was subdural hematoma, whereas fractures were the most common injuries in the motor vehicle collision group. The most frequent complications included urinary tract infections and aspiration pneumonias. Neither age nor MOI was correlated with injury severity. Increasing age and injury severity were predictors for complications and mortality while in hospital. CONCLUSIONS: Despite severe injuries, most elderly patients can survive traumatic injuries. The data suggest that, although elderly patients are prone to incur complications and have greater risk of dying as a result of their injuries, most of these patients will survive their traumatic accidents. The data also show that nosocomial complications play a significant role in the risk of mortality in elderly trauma victims.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.350
Threshold uncertainty score0.704

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations99
Published2007
Admission routes2
Has abstractyes

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