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Record W1991050057 · doi:10.1080/02699050903200555

The heterogeneity of mild traumatic brain injury: Where do we stand?

2009· article· en· W1991050057 on OpenAlexafffund
Andrée Tellier, Shawn Marshall, Keith G. Wilson, Andra Smith, Mary Perugini, Ian G. Stiell

Bibliographic record

VenueBrain Injury · 2009
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of OttawaOttawa Hospital
FundersOntario Neurotrauma Foundation
KeywordsTraumatic brain injuryNeuropsychologyAmnesiaNeuropsychological assessmentPsychologyMedicinePoison controlNeuropsychological testPhysical therapyPsychiatryCognitionEmergency medicine

Abstract

fetched live from OpenAlex

PRIMARY OBJECTIVE: To explore the heterogeneity of mild traumatic brain injury (mTBI). METHODS AND PROCEDURES: Hospital-based prospective follow-up study of 125 patients with mTBI sub-divided into 'severity' sub-groups on the basis of GCS scores (GCS of 15 = mild sub-group; GCS of 13-14 = moderate sub-group). Post-traumatic amnesia (PTA) duration (30 minutes used as a cut-off) was also used to define group membership for secondary analyses. The follow-up assessment consisted of a brief neuropsychological battery as well as measures of neurobehavioural functioning, community integration and post-concussive symptomatology. CT scanning was also obtained when clinically relevant. MAIN OUTCOMES AND RESULTS: The two mTBI sub-groups, as defined by GCS scores, did not differ with respect to post-concussive symptomatology, neurobehavioural symptoms, neuropsychological performance or CT scan abnormalities. In contrast, when group membership was redefined on the basis of PTA, the two sub-groups differed significantly with respect to intracranial abnormalities and report of aggressive or disinhibited behaviours at the 6-month mark. CONCLUSIONS: While the notion of heterogeneity in mTBI was not supported when severity was based on GCS scores, there was partial support when PTA duration was used as a measure of severity.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.082
GPT teacher head0.383
Teacher spread0.301 · 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.

Study designNot applicable
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

Citations57
Published2009
Admission routes2
Has abstractyes

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