Stress et prévention de la récurrence de la maladie coronarienne
Bibliographic record
Abstract
A good deal of recent research suggests that a period of mounting life stress is a precursor of many physical illnesses, including episodes of coronary heart disease (CHD). It should be possible then, by monitoring levels of stress to predict when a high risk individual is likely to suffer a further illness episode, and in some cases to prevent the episode by alleviating stress producing problems. Based on this concept, we have telephone-monitored (at monthly intervals) 37 CHD patients discharged from the coronary unit of the Montreal General Hospital. Stress was measured using a 20-item, self-report scale (Goldberg), and charted for each patient over a seven month period. When a patient's stress rose above a critical level he received a home visit by the project nurse, who investigated his problems and attempted to alleviate them. Interventions varied from simple reassurance to referral for psychiatric treatment or legal aid. Monitoring stress in this way revealed a picture remarkably like the theoretical model. None of the 15 consistently low-scoring patients required rehospitalization. Eleven patients had low scores at the time of discharge, but their scores rose above the critical level in subsequent months. Nine of them responded in a gratifying way- to the home visit and subsequent intervention by the nurse, and none required rehospitalization. The one patient of this type who did require hospitalization had not received a home visit because no nurse was available at the time. Four of the nine patients with consistently high scores required eight rehospitalization s for CHD episodes. These patients seemed to be chronically stressed, and often required continuous support from the nurse. Our study suggests that life stress may be more important than the traditional physical risk factors (obesity, smoking, hypertension, family history of CHD, lack of exercise) in the etiology of recurrent CHD when patients receive adequate medical care. Some of our findings suggest that the nurse's interventions do reduce rehospitalizations, but a large scale controlled study is called for. We conclude that this technique is worth further evaluation, both as a research method and as a practical device for the prevention of rehospitalization of CHD patients and of other types of episodic illnesses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".