Corrigendum to “Effectiveness of Comprehensive Disease Management Programmes in Improving Clinical Outcomes in Heart Failure Patients. A Meta-Analysis” [Eur J Heart Fail 7 (2005) 1133—1144]
Bibliographic record
Abstract
The Authors and Publisher regret to inform you that in the originally published article, the incorrect version of Figures 4 and Fig. 6 were used. Please find the correct versions over. Please note the following text from the authors with respect to Figure 6: “Our paper appeared as an “article in press” on the Journal website on September 29, 2005, and since then we received several comments by your readers. One of the readers raised some questions about Figure 6 and the data shown in it (page 1140 of the printed version) and we decided to review our original analysis. We realised the raw data were entered in the analysis programme in a wrong way, so that the derived results were mistakenly interpreted as if they favoured the control treatment and not the intervention — the disease management programmes (DMP). However, this error did not materially affect the overall conclusion of the paper. Instead, after rectifying the error, the revised analysis provides an estimate of the intervention effect that even more clearly favours DMP. Specifically, the number of patients NOT receiving ACE-inhibitors is significantly lower in the intervention (DMP) group than in the control (usual care) group, that is DMP favours the use of ACE-inhibitors: odds ratio=0.69 (95% confidence interval: 0.56–0.86, p=0.0007).” Apologies for any inconvenience caused.
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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 | 0.011 | 0.121 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.121 | 0.022 |
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".