Self-Monitoring by Traffic Light Color Coding Versus Usual Care on Outcomes of Patients With Heart Failure Reduced Ejection Fraction: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Patients with heart failure (HF) reduced ejection fraction (HFrEF) have symptoms that are more severe and experience a higher rate of hospitalization compared with HF preserved ejection fraction (HFpEF) patients. However, symptom recognition cannot be made by patients based on current approaches. This problem is a barrier to effective self-care that needs to be improved by new self-monitoring instruments and strategies. OBJECTIVE: This study describes a protocol for the self-monitoring daily diaries of weight and shortness of breath (SOB) based on the traffic light system (TLS). The primary objective is to compare the self-care between the intervention and control group. Comparison of HF knowledge, HF quality of life (HFQOL), and all-cause hospitalization between the 2 groups are the secondary objectives. METHODS: A single-blind randomized controlled trial is being conducted at the HF clinic at Tehran Heart Center (Tehran, Iran). Sixty-eight adult patients of both genders will be enrolled during admission to HF clinic. Eligible subjects will be assigned to either the intervention or control group by a block balanced randomization method. Baseline surveys will be conducted before random allocation. Participants in the intervention group will receive an integrated package consisting of (1) HF self-care education by an Australian Heart Foundation booklet on HF, (2) regular home self-monitoring of weight and SOB, and (3) scheduled call follow-ups for 3 months. Patients in the control group will receive no intervention and they only complete monthly surveys. RESULTS: This study is ongoing and is expected to be completed by the end of 2018. CONCLUSIONS: This is the first trial with new self-monitoring instruments in Iran as a low and middle-income country. If the findings show a positive effect, the package will be applied in different regions with the same health care status. TRIAL REGISTRATION: Iranian Registry of Clinical Trials IRCT2017021032476N1; https://en.irct.ir/trial/25296?revision=25296 (Archived by WebCite at http://www.webcitation.org/73DLICQL8). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/9209.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.059 | 0.008 |
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".