The MacNew Questionnaire Is a Helpful Tool for Predicting Unplanned Hospital Readmissions After Coronary Revascularization
Bibliographic record
Abstract
BACKGROUND: The MacNew questionnaire is a neuro-behavioral tool which is easy and immediately usable. This self-reported questionnaire filled out by the patient allows the physician to achieve helpful information concerning the ways for optimizing the therapy and patient's lifestyles. In this retrospective study, our aim was to assess whether relatively high scores found using the MacNew questionnaire in patients who had undergone percutaneous or surgical revascularization were associated with a decreased risk of unscheduled hospitalizations during the follow-up. METHODS: A retrospective analysis concerning 210 patients was carried out. The clinical sheets of these patients were examined as regards the information provided in the specific questionnaires (MacNew Italian version) routinely administered during the hospitalization prescribed for recovering from recent interventions of coronary percutaneous or surgery revascularization. Every patient undergoing the psychological test with MacNew questionnaire was followed up for 3 years. RESULTS: Using univariate analysis, a global score's high value (i.e., above the median of the whole examined population) was shown to be associated with a significantly decreased risk of rehospitalization (HR (hazard ratio): 0.4312; 95% CI: 0.3463 - 0.5370; P < 0.0001). After adjustment for age, gender and myocardial infarction as initiating event, using a multivariate Cox proportional hazards regression model, the protection exerted by a high MacNew score against the risk of hospitalizations remained significant (HR: 0.0885; 95% CI: 0.0317 - 0.2472; P < 0.0001). CONCLUSIONS: A relatively elevated MacNew global score appears to be associated with a significantly decreased risk of unscheduled hospitalizations after coronary revascularization over a 3-year follow-up.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".