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Record W2058514474 · doi:10.1080/0269905031000070242

Relationships between olfactory discrimination and head injury severity

2003· article· en· W2058514474 on OpenAlexaff
Paul Green, Martin L. Rohling, Grant L. Iverson, Roger O. Gervais

Bibliographic record

VenueBrain Injury · 2003
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsRiverview HospitalUniversity of British Columbia
Fundersnot available
KeywordsAmnesiaGlasgow Coma ScaleNeuropsychologyHead injuryPsychologyTraumatic brain injuryClosed head injuryComa (optics)MedicineHead traumaNeuropsychological testAudiologyPsychiatrySurgeryCognition

Abstract

fetched live from OpenAlex

The goal of this study was to examine the relationship between brain injury severity and scores on both an olfactory identification test and on many widely used neuropsychological tests in 367 patients with head injuries of varying levels of severity. It was hypothesized that valid olfactory test scores would correlate highly with injury severity because both the olfactory nerves and the primary olfactory cortices are especially vulnerable to damage in closed head injury. After removing data of doubtful validity from cases failing effort tests, olfactory test scores were related to Glasgow Coma Scale scores (GCS), post-traumatic amnesia and radiological abnormalities more strongly than any of the neuropsychological test scores. Based on the assumption that post-traumatic amnesia is caused by a different mechanism than loss of core consciousness, it was also predicted that there would be no cases with a GCS less than 13 and with no post-traumatic amnesia. As predicted, there were no cases in this group. The results support previous studies showing greater olfactory impairment with increased severity of head injury.

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)0.004
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
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 science0.0000.001
Research integrity0.0000.001
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 head0.252
GPT teacher head0.321
Teacher spread0.069 · 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

Citations86
Published2003
Admission routes1
Has abstractyes

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