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Record W2905934369 · doi:10.4081/mi.2018.7901

Scale for retrospective assessment of immediate concussion symptoms

2018· article· en· W2905934369 on OpenAlexaff
Zack Z. Cernovsky, Paul Istasy, Y Bureau, Simon Chiu

Bibliographic record

VenueMental Illness · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsWestern University
Fundersnot available
KeywordsRivermead post-concussion symptoms questionnaireConcussionCronbach's alphaMedicinePost-concussion syndromeRetrospective cohort studyScale (ratio)FeelingPhysical therapyPsychiatryClinical psychologyInjury preventionPsychologyPoison controlTraumatic brain injuryPsychometricsEmergency medicineSurgeryCartography

Abstract

fetched live from OpenAlex

The retrospective diagnosis of concussion is often missed by clinicians. We present a brief scale for retrospective assessment of the immediate concussion symptoms (ICS) to facilitate the diagnosis of patients without visible head injury or full loss of consciousness. We administered the scale to 90 survivors of car accidents (mean age 42.0, SD=13.6; 33 males, 57 females) at 2 to 33 months after their accident. Our scale consists of 6 items and these were endorsed by the following % of our respondents: feeling dazed (64.4% of our 90 respondents), stunned (73.3%), confused (70.0%), disoriented (62.2%), dizzy (57.8%), and loss of consciousness (22.2%). The statistical properties of the scale are satisfactory (Cronbach alpha = 0.74). The scale correlates with post-accident insomnia (r=0.28), depression (r=0.29), and also with Rivermead measure of the chronic post-concussion syndrome (r=0.34). The ICS scale could be used as a starting point in longitudinal research with brain imaging procedures to evaluate the stages of recovery from the initial concussion. Attached are the English, Spanish, French, German, Italian, Russian, and Czech versions of our scale.

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)0.000
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 score0.158
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.030
GPT teacher head0.383
Teacher spread0.353 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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