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Hypotension and Adverse Outcomes in Moderate to Severe Traumatic Brain Injury

2024· review· en· W4404229306 on OpenAlexafffund
Jun Won Lee, Wendy Wang, Amal Rezk, Ayman A. Mohammed, Kyle Macabudbud, Marina Englesakis, Abhijit V. Lele, Frederick A. Zeiler, Tumul Chowdhury

Bibliographic record

VenueJAMA Network Open · 2024
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsOntario Brain InstituteUniversity Health NetworkToronto Western HospitalUniversity of TorontoUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsTraumatic brain injuryMedicineAnesthesiaAdverse effectEmergency medicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Importance: Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. Hypotension in patients with TBI is associated with poorer outcomes. A comprehensive review examining adverse outcomes of hypotension in patients with TBI is needed. Objective: To investigate the mortality and incidence of hypotension in patients with TBI. Data Sources: A search of studies published before April 2024 was conducted using MEDLINE, MEDLINE In Process, ePubs, Embase, Classic+Embase, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews for primary research articles in English, including randomized control trials, quasirandomized studies, prospective cohorts, retrospective studies, longitudinal studies, and cross-sectional surveys. Study Selection: Inclusion criteria were patients aged at least 10 years with moderate to severe TBI with hypotension. The exclusion criteria were mild TBI (due to the differences in management principles from moderate to severe TBI). Data were screened using Covidence software with multiple reviewers. Data Extraction and Synthesis: This meta-analysis conforms to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guidelines for assessing data quality and validity. Primary outcomes (unadjusted and adjusted odds ratios [ORs]) were calculated using a random-effect model with 95% CIs. Incidence of hypotension was derived using logit transformation. Main Outcomes and Measures: Main outcomes were association of hypotension with death and/or vegetative state within 6 months and incidence of hypotension. Vegetative state was not reported due to lack of data from included studies. Hypothesis testing occurred before data collection. Results: The search strategy identified 17 676 unique articles. The final review included 51 studies (384 329 patients). Pooled analysis of found a significant increase in mortality in patients with hypotension and moderate to severe TBI (crude OR, 3.82; 95% CI, 3.04-4.81; P < .001; I2 = 96.98%; adjusted OR, 2.22; 95% CI, 1.96-2.51; P < .001; I2 = 92.21%). The overall hypotension incidence was 18% (95% CI, 12%-26%) (P < .001; I2 = 99.84%). Conclusions and Relevance: This meta-analysis of nearly 400 000 patients with TBI found a significant association of greater than 2-fold odds of mortality in patients with hypotension and TBI. This comprehensive analysis can guide future management recommendations, specifically with respect to blood pressure threshold management to reduce deaths when treating patients with 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 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)0.019
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.060
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.014
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.080
GPT teacher head0.370
Teacher spread0.290 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations29
Published2024
Admission routes2
Has abstractyes

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