MétaCan
Menu
Back to cohort
Record W2147779234 · doi:10.1080/02699050050043980

Effect of depression on neuropsychological functioning in head injury: measurable but minimal

2000· article· en· W2147779234 on OpenAlexaff
Esther Str Elisabeth M. S. Sherman

Bibliographic record

VenueBrain Injury · 2000
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsBC Children's HospitalRiverview HospitalUniversity of Victoria
Fundersnot available
KeywordsNeuropsychologyPsychologyDepression (economics)Psychomotor learningClinical psychologyNeuropsychological assessmentHead injuryTraumatic brain injuryPoison controlCognitionPsychiatryMedicine

Abstract

fetched live from OpenAlex

The goals of the study were to determine how neuropsychological functioning is related to depressive status in persons with head injury, and to quantify this relationship from a clinically relevant standpoint. Participants were 175 adults involved in litigation, referred for evaluation of suspected head injury. Depression status was measured using the Depression Content (Dep) scale of the MMPI-2. Depression status was related to measures of visual attention and psychomotor skills, but not to other neuropsychological domains such as verbal ability, visual-spatial reasoning, or encoding/organization. However, differences between low Dep and high Dep groups were minimal from a clinical standpoint. Depression appeared to contribute to an increased risk of impaired neuropsychological performance across domains, but only in persons not severely compromised by neuropsychological deficits. Overall, the results indicated a small effect of depression on neuropsychological functioning that is likely only detectable in persons whose neuropsychological compromise is relatively minimal.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.055
GPT teacher head0.374
Teacher spread0.319 · 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

Citations25
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueBrain InjurySame topicTraumatic Brain Injury ResearchFrench-language works237,207