Communication strategies for rare cancers: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Rare cancers comprise almost a quarter of all cancers in Europe, and patients generally have poorer outcomes than those suffering from more common cancers. This is attributed in part to a general lack of knowledge and awareness of rare cancers. This review aims to examine the communication strategies being used throughout the world to inform on rare cancers and to highlight any opportunities for improvement. METHODS: A systematic review of literature published in English prior to November 2018 will be conducted, screening articles from the electronic databases MEDLINE, PubMed, EMBASE, Web of Science, PsycINFO, CINAHL Plus and the Cochrane Database of Systematic Reviews. Grey literature databases (GreyLit, OpenGrey) will also be searched in order to screen for any unpublished works. As well as primary literature, reference lists will be examined via forward and reverse citation screening. The review will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). Titles and abstracts will first be examined for eligibility, with remaining studies undergoing a full-text screening before being included in the final review. Individual studies will be screened for bias, and a meta-analysis performed provided there is enough data. If insufficient homogenous literature exists, a narrative summary of the literature will be produced. DISCUSSION: Despite the broad topic and width of study type that will be considered, this review hopes to provide a reflective summary of the communication strategies available for people living with and working with rare cancer. It aims to reveal any gaps in the resources available, to contribute to the long-term improvement of diagnosis and management of rare cancers. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018099784.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".