Characteristics and impact of real-world evidence studies in oncology: comprehensive mapping review of publications evaluating targeted therapies in solid tumours
Bibliographic record
Abstract
Background: A mapping review of real-world evidence (RWE) publications on targeted therapy (TT) for solid tumours was carried out to describe their characteristics, strengths, and limitations. Methods: RWE publications were identified that: (i) focused on TTs in patients with solid tumours; (ii) included study objectives of effectiveness, predictive or prognostic factors, safety or quality of life; (iii) were published between 1 January 2020 and 22 December 2022. Associations between study variables and journal impact factor (IF) were explored through regression and cluster network analyses. Results: Of 7775 publications identified, 1251 were eligible for analysis. The number of publications per year increased over time. Most studies were conducted in Asia (50%), Europe (25%), and North America (17%), with only 8% in more than one country. Data sources were mostly health records (55%) and registries (11%). Most studies were retrospective (85%) and only 16% were population based. Gastrointestinal tumours were the most frequently studied (30%), followed by lung (22%) and breast (21%). The median journal IF was 4.4. Involvement of >10 centres and studies originating from Europe were significantly associated with a higher IF (≥7) in multivariable analysis. Network analysis demonstrated strong associations between countries and the number of publications in specific tumour types. Conclusions: The number of RWE publications on TT for solid tumours is increasing, but studies are heterogeneous, mostly retrospective, and published in low IF journals. International collaboration and promotion of standardised data sources is imperative to enhance the relevance of RWE research to complement clinical guidelines and impact clinical practice.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".