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Record W2019015558 · doi:10.1097/htr.0b013e3181cd67ea

Outcome in Tracheostomized Patients With Severe Traumatic Brain Injury Following Implementation of a Specialized Multidisciplinary Tracheostomy Team

2010· article· en· W2019015558 on OpenAlexaffabout
Joanne LeBlanc, Judith Robillard Shultz, Alena Seresova, Élaine de Guise, Julie Lamoureux, Nancy Fong, Judith Marcoux, Mohammad Reza Maleki, Kosar Khwaja

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2010
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsMcGill University Health CentreMontreal General Hospital
Fundersnot available
KeywordsMultidisciplinary teamGlasgow Outcome ScaleMedicineMultidisciplinary approachGlasgow Coma ScaleRetrospective cohort studyEmergency medicinePhysical therapyPopulationTraumatic brain injuryAnesthesiaSurgeryNursingPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the effect of a specialized multidisciplinary tracheostomy team on outcome of patients with severe traumatic brain injury (sTBI). DESIGN: Retrospective study with historical controls. PARTICIPANTS: Twenty-seven patients with sTBI tracheostomized before implementation of the tracheostomy team approach and 34 patients followed by the team. SETTING: A regional level 1 tertiary care trauma center, McGill University Health Centre-Montreal General Hospital. MAIN OUTCOME MEASURES: Time to decannulation, length of stay (LOS), Passy-Muir speaking valve use, and extended Glasgow Outcome Scale (GOS-E) scores given at acute care discharge. RESULTS: The groups were similar for injury severity, age, and premorbid health conditions. Postteam patients had a significantly shorter LOS (P = .025) and more of them used Passy-Muir speaking valves (P = .004). Furthermore, there was a trend toward decreased time to decannulation in the postteam group. GOS-E scores did not differ significantly between groups (P > .05). CONCLUSION: Implementation of the tracheostomy team appears to have had positive clinical benefits for this population.

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)0.001
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 score0.102
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.343
Teacher spread0.329 · 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

Citations32
Published2010
Admission routes2
Has abstractyes

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