MétaCan
Menu
Back to cohort
Record W2154211982 · doi:10.1017/s1355617706060036

Evaluating the utility of ICD-10 diagnostic criteria for postconcussion syndrome following mild traumatic brain injury

2006· article· en· W2154211982 on OpenAlexaff
Shauna Kashluba, Joseph E. Casey, Chris Paniak

Bibliographic record

VenueJournal of the International Neuropsychological Society · 2006
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of Windsor
Fundersnot available
KeywordsTraumatic brain injuryMedicineDepression (economics)VertigoPost-concussion syndromeAnxietyInjury preventionPhysical therapyPoison controlConcussionPsychiatryEmergency medicineSurgery

Abstract

fetched live from OpenAlex

The present study investigated the utility of the International Classification of Diseases and Related Health Problems, 10th edition (ICD-10) diagnostic criteria for postconcussion syndrome (PCS) symptoms by comparing symptom endorsement rates in a group of patients with mild traumatic brain injury (MTBI) to those of a noninjured control group at one month and three months post-injury. The 110 MTBI patients and 118 control participants were group-matched on age, gender, and education level. Seven of the nine self-reported ICD-10 PCS symptoms differentiated the groups at one month post-injury and two symptoms differentiated the groups at three months post-injury: symptom endorsement rates were higher in the MTBI group at both time periods. Fatiguing quickly and dizziness/vertigo best differentiated the groups at both time periods, while depression and anxiety/tension failed to differentiate the groups at either time period. Collectively, the ICD-10 PCS symptoms accurately classified the MTBI patients at one month post-injury, with the optimal positive test threshold of endorsement of five symptoms coinciding with a sensitivity and specificity of 73% and 61%, respectively. The ICD-10 PCS symptoms were unable to accurately classify the MTBI patients at three months post-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 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)0.003
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.902

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.183
GPT teacher head0.475
Teacher spread0.292 · 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

Citations93
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the International Neuropsychological SocietySame topicTraumatic Brain Injury ResearchFrench-language works237,207