Investigating strategies to improve clinical trial opportunities in oncology in New Zealand (INSIGHT).
Bibliographic record
Abstract
e18851 Background: Despite the importance of clinical trials, fewer than 5% of adult cancer patients are enrolled in them. Multiple patient (pt), clinician and institutional barriers have been identified. This study aimed to explore what factors impact access to cancer trials in NZ. Methods: After ethics approval, a 28-point survey was circulated via 9 DHBs and 4 cancer foundations to pts with a cancer diagnosis. Questions covered demographics and potential factors that might impact participation in trials e.g. travel. A 23-point survey was sent to research staff across the 9 DHBs. Results: Between July 2016 and June 2017, 691 pts responded; 63% were female, 77% were over the age of 50. Most pts (89%) knew of clinical trials and 86% would consider participating in a trial. The proportion did not differ by age, location, income, tumor type or gender. 44% would consider treatment at a different hospital, and 11% would consider relocating for a trial. 130 pts (19%) had been on a trial of whom only 3 pts (2%) would not consider a trial in the future. Only 10% thought trials should be a last resort. Participation factors seen as advantages included: benefiting others (such as doctors’ research) (93%), better treatment (70%), more scans and longer follow-up (51%). Disincentives for participation included fear of randomization (78%), treatment toxicities (72%), time and cost of more visits (40%) and unspecified future use of tissue (33%). Of 111 responses from research staff, 54% had experience as either a principal investigator (PI) or sub-PI. The 3 most commonly reported barriers by research staff were time (73%), money (70%) and infrastructure (49%). Researchers felt additional tests, language issues and pt awareness of trials were key barriers to pt participation. Conclusions: The identified barriers to trial participation appear similar in NZ to other developed countries. In this motivated cohort, many pts did not mind extra travel or tests, in contrast to perceptions of the research team. There is a strong interest from pts with cancer to consider participation in trials at any stage of their treatment. This suggests that addressing the areas of clinician and infrastructure barriers may help improve access to clinical trials in NZ.
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 | 0.129 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.006 |
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".