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Record W2082007483 · doi:10.1080/13854046.2010.506199

Comprehensive Clinical Picture of Patients with Complicated vs Uncomplicated Mild Traumatic Brain Injury

2010· article· en· W2082007483 on OpenAlexaff
Élaine de Guise, Jean‐François Lepage, Simon Tinawi, Joanne LeBlanc, Jehane H. Dagher, Julie Lamoureux, Mitra Feyz

Bibliographic record

VenueThe Clinical Neuropsychologist · 2010
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsMcGill UniversityUniversité de MontréalMcGill University Health CentreMontreal General Hospital
Fundersnot available
KeywordsTraumatic brain injuryNeuropsychologyMedicineNeurological examinationPost-concussion syndromeNeuropsychological assessmentPhysical examinationVestibular systemNeuroimagingPediatricsPoison controlPhysical therapyCognitionConcussionInjury preventionInternal medicineSurgeryAudiologyEmergency medicinePsychiatry

Abstract

fetched live from OpenAlex

To compare the acute clinical profile of patients with uncomplicated vs complicated mild TBI (MTBI), socio-demographic and medical history variables were gathered for 176 patients diagnosed with MTBI and with (complicated, N = 45) or without (uncomplicated, N = 131) positive findings on cerebral imaging. Neurological examination, neuropsychological assessment and self-evaluation of post-concussive symptoms were done at 2 weeks post trauma. Patients with complicated MTBI were more likely to show auditory and vestibular system dysfunction. Surprisingly, the uncomplicated group reported more severe post-concussive symptoms than patients with positive CT scans. The groups showed no other difference in neurological, psychological, or cognitive outcome. A complete neurological examination should be done acutely in patients with MTBI to determine more specific follow-up required.

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 score0.002
Threshold uncertainty score0.008

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.247
GPT teacher head0.481
Teacher spread0.234 · 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

Citations56
Published2010
Admission routes1
Has abstractyes

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