High trait anger increased stroke in people ≤60 years and those with high density lipoprotein cholesterol concentrations >47 mmol/l
Bibliographic record
Abstract
Williams JE, Nieto FJ, Sanford CP, et al. The association between trait anger and incident stroke risk: the Atherosclerosis Risk in Communities (ARIC) Study. Stroke2002 Jan; 33 : 13 –20 [OpenUrl][1][Abstract/FREE Full Text][2] QUESTION: What is the relation between trait anger and incident stroke risk? How is this relation affected by selected risk factors? Population based cohort study with median follow up of 77.3 months. Communities in Maryland, Minnesota, North Carolina, and Mississippi, USA. 13 851 people who were 48–67 years of age (mean age 57 y, 56% women, 76% white). The frequency and degree to which participants had anger was assessed using the Spielberger Trait Anger Scale. High trait anger was defined by scores of 22–40, moderate anger by scores of 15–21, and low anger by scores of 10–14. Other risk factors assessed included blood pressure, waist to hip ratio, diabetes, alcohol consumption, cigarette … [1]: {openurl}?query=rft.jtitle%253DStroke%26rft.stitle%253DStroke%26rft.aulast%253DWilliams%26rft.auinit1%253DJ.%2BE.%26rft.volume%253D33%26rft.issue%253D1%26rft.spage%253D13%26rft.epage%253D20%26rft.atitle%253DThe%2BAssociation%2BBetween%2BTrait%2BAnger%2Band%2BIncident%2BStroke%2BRisk%253A%2BThe%2BAtherosclerosis%2BRisk%2Bin%2BCommunities%2B%2528ARIC%2529%2BStudy%2B%2A%2BEditorial%2BComment%253A%2BThe%2BAtherosclerosis%2BRisk%2Bin%2BCommunities%2B%2528ARIC%2529%2BStudy%26rft_id%253Dinfo%253Adoi%252F10.1161%252Fhs0102.101625%26rft_id%253Dinfo%253Apmid%252F11779882%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/ijlink?linkType=ABST&journalCode=strokeaha&resid=33/1/13&atom=%2Febmental%2F5%2F3%2F94.atom
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".