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Record W2020954880 · doi:10.3109/02699050903446815

Traumatic brain injury and olfactory deficits: The tale of two smell tests!

2009· article· en· W2020954880 on OpenAlexaffabout
Audrey Fortin, Mathilde Beaulieu Lefebvre, Maurice Ptito

Bibliographic record

VenueBrain Injury · 2009
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsCentre de réadaptation Lethbridge-Layton-MackayUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersUniversity of Pennsylvania
KeywordsTraumatic brain injuryOlfactory perceptionOlfactionOlfactory systemPsychologyNeurosciencePoison controlInjury preventionMedicineAudiologyPsychiatryMedical emergency

Abstract

fetched live from OpenAlex

PRIMARY OBJECTIVE: Olfactory functions are not systematically evaluated following traumatic brain injury (TBI). This study aimed at comparing two smell tests that are used in a clinical setting. RESEARCH DESIGN: The University of Pennsylvania Smell Identification Test (UPSIT) and the Alberta Smell Test were compared in terms of assessment time, cost and diagnosis. Parameters associated with olfactory loss such as injury severity, type of cerebral lesion and depressive data were considered. Forty-nine TBI patients admitted to an outpatient rehabilitation programme took part in this experiment. RESULTS: The scores of the two smell tests were significantly correlated. Both tests indicated that patients with frontal lesion performed significantly worse than patients with other types of lesion. Mood and injury severity were not associated with olfactory impairment when age was taken into account. Between 40-44% of the patients showing olfactory impairments were not aware of their deficit. CONCLUSIONS: Since a significant proportion of the patients showing olfactory impairments were not aware of their deficit, it is recommended than clinicians systematically evaluate olfactory functions using the Alberta Smell test. To refine their diagnosis, the UPSIT can also be used.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.000
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 head0.120
GPT teacher head0.311
Teacher spread0.190 · 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 designBench or experimental
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

Citations46
Published2009
Admission routes2
Has abstractyes

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