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Record W2148982081 · doi:10.1136/ebmh.7.3.88

Traumatic brain injury increases the risk of psychiatric illness

2004· letter· en· W2148982081 on OpenAlexaff
Anthony Feinstein

Bibliographic record

VenueEvidence-Based Mental Health · 2004
Typeletter
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSunnybrook Health Science CentreWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineTraumatic brain injuryIncidence (geometry)IMGWeb of sciencePopulationPediatricsInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Fann JR, Burington B, Leonetti A, et al . Psychiatric illness following traumatic brain injury in an adult health maintenance organization population. Arch Gen Psych 2004;61:53–61.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q Does traumatic brain injury increase the risk of psychiatric illness? ### ![Graphic][5] Design: Prospective cohort study. ### ![Graphic][6] Follow up period: Three years. ### ![Graphic][7] Setting: Puget Sound area, Washington State, USA; recruited in 1992. ### ![Graphic][8] People: 939 people, aged >15 years, diagnosed with traumatic brain injury according to ICD-9-CM criteria. For each case of traumatic brain injury three people matched for sex, age, and diagnosis date were selected as controls. Exclusions: incomplete medical records for the preceding year; previous traumatic brain injury. ### ![Graphic][9] Risk factors: Mild traumatic brain injury; moderate to severe traumatic brain injury (ICD-9-CM). ### ![Graphic][10] Outcomes: Incidence of any psychiatric disorder determined by diagnosis (ICD-9-CM), … [1]: {openurl}?query=rft.jtitle%253DArchives%2Bof%2BGeneral%2BPsychiatry%26rft.stitle%253DArch%2BGen%2BPsychiatry%26rft.aulast%253DFann%26rft.auinit1%253DJ.%2BR.%26rft.volume%253D61%26rft.issue%253D1%26rft.spage%253D53%26rft.epage%253D61%26rft.atitle%253DPsychiatric%2BIllness%2BFollowing%2BTraumatic%2BBrain%2BInjury%2Bin%2Ban%2BAdult%2BHealth%2BMaintenance%2BOrganization%2BPopulation%26rft_id%253Dinfo%253Adoi%252F10.1001%252Farchpsyc.61.1.53%26rft_id%253Dinfo%253Apmid%252F14706944%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1001/archpsyc.61.1.53&link_type=DOI [3]: /lookup/external-ref?access_num=14706944&link_type=MED&atom=%2Febmental%2F7%2F3%2F88.atom [4]: /lookup/external-ref?access_num=000187894000006&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif [8]: /embed/inline-graphic-4.gif [9]: /embed/inline-graphic-5.gif [10]: /embed/inline-graphic-6.gif

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.002

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 head0.093
GPT teacher head0.395
Teacher spread0.302 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2004
Admission routes1
Has abstractyes

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