[Do critical pathways improve outcomes of patients with cardiac failure?].
Bibliographic record
Abstract
UNLABELLED: Cardiac failure represents an important public health problem and despite recent clinical, diagnostic and therapeutic advances, the incidence and prevalence of this syndrome show a steady increase. In view of this, the authors conducted a meta-analysis to evaluate the effect of critical pathways in the management of patients with cardiac failure when compared with standard care. The impact of critical pathways on the following outcomes were evaluated: hospital mortality, mortality at six months, mean length of hospital stay, direct costs, readmission rates at one, three and six months. METHODS: The following databases were consulted: Medline, Embase, CINAHL, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews. The research was limited to articles published between January 1975 and June 2010. Methodological quality of studies was evaluated by the Jadad method (for RCTs, cRCT, CCT) and the New Castle Ottawa Scale for case-control and cohort studies. Data analysis was performed by using the statistical methods described in the Cochrane Collaboration guidelines. Meta-analyses were performed using RevMan software version 5. RESULTS: Eleven studies were included in the meta-analysis (5,460 patients). A lower mortality (hospital mortality and mortality at 6 months) was observed in the critical pathways group compared to the group treated with standard care. A positive impact of critical pathways was also observed in length of stay, direct costs, readmission after one, three and six months. CONCLUSIONS: Critical pathways can improve the quality of care provided to patients with cardiac failure. Further studies are needed to evaluate which mechanisms within the care pathways can truly improve the quality of care.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".