Type-D Personality and Heart Disease: It Might Be ‘One Small Step’, but It Is Still Moving Forward: A Comment on Grande et al.
Bibliographic record
Abstract
Since the landmark studies of the late 60s and early 70s, we as a field have tried to find the key psychosocial predictors of poorer cardiovascular outcomes. In the mid-90s, a new construct, type-D personality, was found to have significant predictive ability for mortality in patients with coronary heart disease [1]. This has led to a constant stream of original studies and reviews exploring the potential role of type-D personality in the progression of heart disease. Of particular note, there has been a recent spate of systematic reviews [2–4] on the topic. In spite of this, the review by Grande et al. [5] truly provides an extension, not only through the inclusion of new studies, but also in its conceptualisation. Grande and colleagues have taken a rigorous and refined approach to assessing the impact of type-D personality on cardiac outcomes, which has created a number of interesting talking points. One particularly interesting point rightly highlighted by the authors is the contrasting data on patients with coronary artery disease vs. those with congestive heart failure (CHF). They raise the issue of whether this difference is driven by a true lack of prognostic effects in CHF patients or if this just reflects the increased sample size in the newer larger CHF studies and tend, with good reason, to lean more on the side of an actual prognostic difference. There is, however, an alternative possibility which revolves around the appropriate cut point for different populations. The type-D scale, which was used in 11 out of the 12 studies, was developed in a specific population, a Flemish/Dutch coronary artery disease population [6], and whilst there has been some good work to establish the factor structure invariance of the scale across different languages and populations [7], the same cannot be said for the cut point of 10. One of the main underpinnings of good psychometrics is that all aspects of a scale or questionnaire are validated when used in new populations, i.e. any population that differs substantially from the original cohort the scale/questionnaire was developed in. Following this, it could be that type-D personality may be predictive of outcomes in CHF populations, but not with a cut point of 10. Further complicating this issue is the way in which the original cut point was generated. The figure of 10 is derived from a median split of negative affect and social inhibition in the original validation study. We think all of us would argue that this is not the optimal way of defining a diagnostic level, though, as highlighted by Grande and colleagues, what is the comparator or ‘reference standard’ [8] needed to determine the classification accuracy of the type-D cut point? Suffice to say, there needs to be more work around the optimisation of a type-D cut point, or if no valid measure can be reliably generated, then only continuous data should be used in the future.
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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 | 0.027 | 0.133 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.056 | 0.070 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".