April 2019 at a Glance: Prediction of Heart Failure, Left Atrial Function, Cardio-Oncology
Bibliographic record
Abstract
Obesity is a well known risk factor for heart failure (HF) development.1 Kokkinos et al.2 assessed the interaction between body mass index and cardiorespiratory fitness in 20 254 US male veterans, who underwent maximal exercise treadmill testing between 1987 and 2017. During a median follow-up of 13.4 years, there were 2979 HF events. Obesity lost its effect as a risk factor for HF after adjustment for fitness level. In contrast, the relation between fitness level and risk of HF remained significant across all the body mass index categories with lower hazard ratios [HRs (95% confidence intervals, CI) 0.37 (0.30–0.47), 0.37 (0.28–0.40) and 0.27 (0.22–0.34)] for high-fit individuals within normal weight, overweight and obese categories, respectively. The role of serum amino-terminal pro-B-type-natriuretic peptide (NT-proBNP) levels for the prediction of HF development was investigated in 3482 subjects aged ≥ 60 years at risk for HF development. HF was diagnosed in 162 participants during a median follow-up of 4.5 years after enrolment. Baseline values of NT-proBNP plasma levels alone had a similar predictive value compared to a multivariable clinical model. NT-proBNP cut-points of 11, 16, and 25 pmol/L for individuals aged 60–69, 70–79, and ≥ 80 years, respectively, achieved sensitivities > 75% and specificities of 47–69% for 5-year prediction of total HF in men and women in all age subgroups.3
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.021 |
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".