Protocol for a systematic review of screening tools for fear of recurrent illness in common life-threatening diseases
Bibliographic record
Abstract
BACKGROUND: A myocardial infarction (MI) ('heart attack') can be intensely stressful, and the impact of this event can leave patients with clinically significant post-MI stress symptoms. Untreated stress can make heart disease worse. Few tools are available that screen for specific thoughts or beliefs that can trigger post-MI stress responses. In other life-threatening illnesses, fear of recurrence (FoR) of illness has been identified as a key stressor, and screening tools have been developed to identify this. The aim of this review is to identify FoR screening tools used in other common life-threatening diseases that report on the development of the tool, to assess if there are any that can be adapted for use in MI survivors so that those with high levels of FoR can be identified and helped. METHODS/DESIGN: The review will evaluate full FoR screening tools and methods of measurement used in common life-threatening disease clinical populations. The Campbell and Cochrane Libraries, Cumulative Index of Nursing and Allied Health Literature (CINAHL), PsycINFO, MEDLINE, Embase, Applied Social Sciences Index and Abstracts (ASSIA), Published International Literature on Traumatic Stress (PILOTS), Social Services Abstracts, Sociological Abstracts, Web of Knowledge, Health and Psychosocial Instruments and SCOPUS databases will be searched for relevant studies published from database inception. Reference lists and published reviews/meta-analyses will also be searched. All titles and abstracts will be screened and relevant full-text versions retrieved by two reviewers, who will then extract all the data. Each will independently review all data extracted by the other. Selected studies will also be assessed by two independent researchers using the COnsensus-based standards for the Selection of health status measurement INstruments (COSMIN) checklist and other quality criteria. This will be done to evaluate the degree to which their measurement properties meet the standards for good methodological quality. Disagreement will be resolved through consensus. DISCUSSION: Untreated post-MI stress has a considerable psychological and physical impact on MI survivors. Therefore, there is a critical need to develop a screening tool to identify fear of recurrent MI so that those affected can be identified and directed to appropriate support interventions. This proposed research will enable a tool to be developed and adapted for use in the MI survivor patient population. PROSPERO: CRD42014010500.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.032 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".