The fear of cancer recurrence literature continues to move forward: a review article
Bibliographic record
Abstract
PURPOSE OF REVIEW: The volume of literature addressing fear of cancer recurrence (FCR) is rapidly increasing. A summary of key developments in the research and treatment of FCR was published by Sharpe et al. in 2017, and the current review focuses on works published thereafter. RECENT FINDINGS: A comprehensive literature review was conducted to provide an up-to-date summary of peer-reviewed publications focusing on FCR. The search consisted of the most recent FCR reports published between 2016 and 2017, which can be broadly categorized as: methods of assessment; associations with FCR; FCR and caregivers; and FCR interventions. SUMMARY: FCR assessments continue to undergo revisions, which may have positive implications for clinicians and researchers seeking shorter measures to assess the FCR of their patients and study participants. However, research is needed to determine if a shorter FCR measure could be created using items that measure the construct alone, yet still retaining optimal sensitivity and specificity, or also with its determinants and consequences. Doing so would result in either unidimensional or multidimensional measure of FCR. Notwithstanding these matters in FCR assessment, the state of the literature continues to advance our understanding about characteristics of survivors with highest FCR, which is useful to identify patients in need of FCR interventions. These empirical results are useful to further investigate the theoretical distinctions of FCR.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".