MétaCan
Menu
Back to cohort
Record W4238138318 · doi:10.14740/jnr283w

Induced Normothermia After Severe Traumatic Brain Injury: A Prospective Observational Pilot Safety and Feasibility Study

2014· article· en· W4238138318 on OpenAlexvenueno aff
Farid Sadaka, Katie Krause, Marianne Tow, M. Elizabeth Wilcox, Jacklyn O’Brien

Bibliographic record

VenueJournal of Neurology Research · 2014
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsShiveringMedicineTraumatic brain injuryAnesthesiaObservational studyHypothermiaProspective cohort studyRandomized controlled trialHyperthermiaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Background: Early hyperthermia following traumatic brain injury (TBI) is associated with worsened neurologic outcomes; however, eliminating fever has not been adequately studied. We sought to explore the safety and feasibility of induced normothermia using advanced fever control (AFC) in severe TBI patients. Methods: Eight patients underwent AFC with a surface cooling device for 96 hours, and four patients underwent conventional fever control (CFC). Average daily fever burden (FB) was calculated as the time and extent (°C × hours) above 37 °C. Shivering was evaluated hourly by the bedside shivering assessment scale (BSAS, 0 - 3) and BSAS > 0 was treated using a stepwise protocol. Results: FB was lower for the AFC vs. CFC group: 4.0 vs. 7.0 (P = 0.2) for day 1, 4.3 vs. 7.7 (P = 0.1) for day 2, 3.6 vs. 5.3 (P = 0.5) for day 3, 4.2 vs. 4.8 (P = 0.7) for day 4 respectively. BSAS > 0 developed more often in the AFC group (130 times) than the CFC group (two times). Conclusion: Induced normothermia was associated with less FB and more interventions to treat shivering compared to CFC in severe TBI patients. A large prospective randomized outcome trial of AFC vs. CFC is warranted. J Neurol Res. 2014;4(5-6):127-132 doi: http://dx.doi.org/10.14740/jnr283w

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)0.006
metaresearch head score (Gemma)0.004
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 score0.025
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
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 integrity0.0000.001
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 head0.200
GPT teacher head0.451
Teacher spread0.251 · 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.

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

Citations2
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Neurology ResearchSame topicThermal Regulation in MedicineFrench-language works237,207