Enhancing the Framingham Risk Score for coronary heart disease by adding information on working hours
Bibliographic record
Abstract
Commentary on: Kivimäki M, Batty GD, Hamer M, et al. Using additional information on working hours to predict coronary heart disease: a cohort study. Ann Intern Med 2011;154:457–63.[OpenUrl][1][CrossRef][2][PubMed][3] The Framingham Risk Score is a widely used prediction model for the assessment of risk for coronary heart disease (CHD), which contains traditional risk factors (age, sex, blood pressure, cholesterol, diabetes and smoking status).1 However, other more non-traditional (psychosocial) factors have also emerged as risks for CHD. The purpose of the study by Kivimäki et al was to determine whether the addition of working hours to the Framingham Score improves the risk prediction of this model. The authors used a prospective cohort of British civil servants enrolled in the Whitehall II study, which examines health behaviour, work environment and socioeconomic status … [1]: {openurl}?query=rft.jtitle%253DAnnals%2Bof%2BInternal%2BMedicine%26rft.stitle%253DANN%2BINTERN%2BMED%26rft.issn%253D0003-4819%26rft.aulast%253DKivimaki%26rft.auinit1%253DM.%26rft.volume%253D154%26rft.issue%253D7%26rft.spage%253D457%26rft.epage%253D463%26rft.atitle%253DUsing%2Badditional%2Binformation%2Bon%2Bworking%2Bhours%2Bto%2Bpredict%2Bcoronary%2Bheart%2Bdisease%253A%2Ba%2Bcohort%2Bstudy.%26rft_id%253Dinfo%253Adoi%252F10.7326%252F0003-4819-154-7-201104050-00003%26rft_id%253Dinfo%253Apmid%252F21464347%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=10.7326/0003-4819-154-7-201104050-00003&link_type=DOI [3]: /lookup/external-ref?access_num=21464347&link_type=MED&atom=%2Febmed%2F17%2F2%2F64.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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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, unvalidatedLabeled directly by 2 models reading the full record.
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".