Alexithymia Affects Pre-Hospital Delay of Patients with Acute Myocardial Infarction: Meta-Analysis of Existing Studies
Bibliographic record
Abstract
BACKGROUND: The time between the onset of symptoms and reperfusion is a critical determinant of the clinical course of patients with acute myocardial infarction (AMI). Any delay in seeking help will affect patient's outcome. Alexithymia can influence the information processing but also the skills to detect the signal of an ongoing AMI. METHOD: Systematic review and meta-analysis of studies investigating the role of alexithymia in pre-hospital delay after AMI. Pubmed/Medline and PsychINFO/Ovid search from 1990 until 2012. RESULTS: Out of 29 studies investigating the role of psychological factors in pre-hospital delay after AMI, 3 studies specifically assessed alexithymia, involving 258 patients. All studies used the Toronto Alexithymia Scale to group patients into clusters by time to presentation after AMI. Meta-analysis of data showed that the patients with higher emotional awareness (i.e., low alexithymia) had shorter time to presentation after AMI. CONCLUSIONS: Preliminary evidence indicates that alexithymia may have a role in seeking help delay after AMI. Further studies are necessary to better appreciate how alexithymia influence help-seeking in patients with an evolving AMI and in what extent their ineffective behavior can be changed.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.037 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".