Psychology and cardiology: do not forget the heart failure patient: reply
Bibliographic record
Abstract
We greatly appreciated the recognition of Dr Jaarsma et al. for our meta-analysis on psychological treatments for cardiac patients, and we wholeheartedly agree that patients with heart failure also deserve, and can possibly benefit from, psychological interventions. We would add that the same is true for patients waiting for heart transplantation, or those with stroke. All three groups are understudied relative to post-MI patients and Linden has already made this point previously.1 The primary reason for focusing on post-MI patients here was the fact that conflicting conclusions had been published over the years and these disagreements may have prevented implementation, or have interfered with continuous operation, of cardiac rehabilitation programmes with psychological treatment components. We believe that our findings have provided needed clarification in this regard and open the door for more effective treatments. Having said that, we speculate that heart failure patients may not benefit as much from psychological treatment as did male post-MI patients because their medical prognosis tends to be objectively worse. Nonetheless, we want to join forces with Dr Jaarsma et al. in calling for more intensive research efforts directed at investigation of psychological treatment effects for all types of cardiovascular disease patients.
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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 | 0.011 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.029 | 0.056 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".