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Evaluation of the effect of intensity of care on mortality after traumatic brain injury

2008· article· en· W2084053481 on OpenAlexaff
Hilaire J. Thompson, Frederick P. Rivara, Gregory J. Jurkovich, Jin Wang, Avery B. Nathens, Ellen J. MacKenzie

Bibliographic record

VenueCritical Care Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversity of Toronto
FundersNational Institute of Nursing ResearchNational Center for Research ResourcesU.S. Public Health Service
KeywordsMedicineGlasgow Coma ScaleTraumatic brain injuryIntensive care unitEmergency medicineInjury Severity ScoreDecompressive craniectomyIntensive carePoisson regressionTrauma centerPopulationCohort studyComorbidityMechanical ventilationPoison controlAnesthesiaInjury preventionIntensive care medicineInternal medicineRetrospective cohort study

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate the effect of age on intensity of care provided to traumatically brain-injured adults and to determine the influence of intensity of care on mortality at discharge and 12 months postinjury, controlling for injury severity. DESIGN: Cohort study using the National Study on the Costs and Outcomes of Trauma (NSCOT) database. Risk ratio and Poisson regression analyses were performed using data weighted according to the population of eligible patients. SETTING AND PATIENTS: A total of 18 level 1 and 51 level 2 non-trauma centers located in 14 states in the United States and 1,776 adults aged 25-84 yrs with a diagnosis of traumatic brain injury. MEASUREMENTS: Injury severity was determined by the motor component of the Glasgow Coma Scale score, the Injury Severity Score, pupillary reactivity, and presence of midline shift. Factors evaluated as contributing to intensity of care included: admission to the intensive care unit, mechanical ventilation, placement of an intracranial pressure monitor, placement of a jugular bulb catheter, placement of a pulmonary artery catheter, critical care consultation, the number of specialty care consultations, mannitol use, treatment with barbiturate coma, decompressive craniectomy, number of nonneurosurgical procedures performed, the presence of a do-not-resuscitate order, and withdrawal of therapy. MAIN RESULTS: Controlling for injury-related factors, sex, and comorbidity, as age increased, the overall likelihood of receiving various interventions decreased. After controlling for injury severity, sex, and comorbidity, factors associated with higher risk of in-hospital death were: being aged 75-84 yrs (relative risk [RR] 1.32, 95% confidence interval [CI] 1.13, 1.55), pulmonary artery catheter use (RR 1.56, 95% CI 1.30, 1.86), intubation (RR 4.17, 95% CI 2.28, 7.61), the presence of a do-not-resuscitate order (RR 3.21, 95% CI 2.21, 4.65), and withdrawal of therapy (RR 2.33, 95% CI 1.69, 3.23). In contrast, a higher number of specialty care consultations (surgical consults: RR 0.63, 95% CI 0.54, 0.74; medical consults: RR 0.87, 95% CI 0.79, 0.95; and other consults: RR 0.43, 95% CI 0.26, 0.69) were associated with decreased risk of death. The results were similar for factors associated with death at 12 months, with the exception that the number of medical consultations was not significant, whereas the number of nonneurosurgical procedures performed was associated with lower risk of death (RR 0.96, 95% CI 0.92, 0.99), as was obtaining critical care consultation services (RR 0.84, 95% CI 0.71, 1.0). CONCLUSIONS: There is a lower intensity of care provided to older adults with traumatic brain injury. Although the specific contributions of specialists to patient management are unknown, their consultation was associated with decreased risk of in-hospital death and death within 12 months. It is important that careproviders have an increased awareness of the potential contribution of multidisciplinary clinical decision making to patient outcomes in older traumatically brain-injured patients.

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)0.005
metaresearch head score (Gemma)0.029
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 score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.405
Teacher spread0.335 · 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

Citations88
Published2008
Admission routes1
Has abstractyes

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