Global survey on barriers to clinical cancer research.
Bibliographic record
Abstract
e16505 Background: Many authorities, including ASCO, have raised concerns about growing barriers to clinical cancer research. We believe this is the first global survey of perceived barriers to clinical cancer research from the investigator perspective. Methods: The ASCO International Affairs Committee invited 300 oncologists in 19 countries to complete a web-based survey. Eighty responded, with 41 from high-income countries (HIC) and 39 from low- and middle-income countries (LMIC). Most responders were medical oncologists (62%), at academic hospitals (90%). Barriers were ranked from 1 (most) to 8 (least) important. Results: Most responders reported participating in academia-driven (ADCT) (92%) and industry-driven clinical trials (IDCT) (89%) in the last 5 years, with a significantly higher proportion from HIC compared to LMIC involved in more than 10 ADCT (71% vs. 29%; p=0.008) and IDCT (71% vs. 29%; p=0.017) in that time. Most of the responders (45% from HIC vs. 55% from LMIC; p=NS) reported “no change” in the last 5 years in the proportion of their trials that were IDCT. Of those who said it has become more difficult to conduct ADCT (40/80) and IDCT (27/80), a significantly higher share came from HIC than from LMIC in the case of both ADCT (29/40; 73% vs. 11/40; 28%; p=0.001) and IDCT (19/27; 70% vs. 8/27; 30%; p=0.009). Average time reported from regulatory initiation to the first-patient-in was up to 90 days for 39% of responders, 90-120 days for 26% and more than 120 days for 30%, with extremes reported most frequently by LMIC responders. A lack of funding was ranked the most important barrier to ADCT by both HIC (3.15) and by LMIC (3.18) responders, and a lack of patients the least important by both HIC (5.27) and LMIC (5.59) responders. LMIC responders considered competent authorities (regulatory) procedures a more important barrier than HIC responders (3.87 vs. 4.67). Conclusions: Though investigators from HIC are involved in more ADCT and IDCT than LMIC counterparts, they are more likely to perceive the barriers to conducting trials as worsening. Of note, no major shift towards greater IDCT was reported in our survey. With regards to ADCT, the main barrier that should be improved globally is financing, with an additional focus in LMIC on regulatory procedures optimization.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".