Survivorship representation at IPOS World Congress: abstract review and analysis
Bibliographic record
Abstract
Abstract Background: Advancements in cancer treatments have enabled more people worldwide to survive cancer, but many experience lasting impacts. The International Psycho-Oncology Society (IPOS) is a global professional organization which hosts an annual World Congress. This study reviewed survivorship content from the World Congress meetings to understand areas of focus, apparent strengths and weaknesses, and global representation. Methods: Peer-reviewed abstracts presented in 2017, 2018, 2019, and 2021 were reviewed. Abstracts were identified by searching for “survivor.” Identified abstracts were read in full to extract content of interest (population of interest, cancer type, number of participants, study design, study topic, first author/country, and international collaboration). Coding was defined a priori. Data were extracted using REDCap. Inter-rater reliability checks were performed. Results: A total of 1813 abstracts were identified and reviewed. The proportion of survivorship-focused abstracts ranged from 13.2%–20.7% annually. Breast cancer dominated survivorship work. The most frequently addressed topics included distress/anxiety/depression (36.6%), quality of life (28.6%), and health behaviors (15.5%). Nearly three-quarters (73%) of abstracts focused on adult populations, and there was apparent international collaboration in 12%–20%. Authorships and abstracts were primarily from high-income countries (91%). Most studies were observational (44%); few were randomized controlled trials (4%). Conclusions: This study found overrepresentation of authorship from some countries. Many topics, patient populations, and countries were not highly represented. IPOS might consider efforts to remedy this imbalance with the ultimate goal of improving psychosocial care for those affected by cancer, globally.
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 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 | 0.028 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.029 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".