Patient enrollment and logistical problems top the list of difficulties in clinical research: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Many medical research projects encounter difficulties. The objective of this study was to assess the self-reported frequency of difficulties encountered by medical researchers while conducting research and to identify factors associated with their occurrence. METHODS: The authors conducted a cross-sectional survey in 2010 among principal investigators of 996 study protocols approved by the Research Ethics Committee in Geneva, Switzerland, between 2001 and 2005. The authors asked principal investigators to rate the level of difficulty (1: none, to 5: very great) encountered across the research process. RESULTS: 588 questionnaires were sent back (participation rate 59.0 %). 391 (66.5 %) studies were completed at the time of the survey. Investigators reported that the most frequent difficulties were related to patient enrollment (44.3 %), data collection (26.7 %), data analysis and interpretation (21.5 %), collaboration with caregivers (21.0 %), study design (20.4 %), publication in peer-reviewed journal (20.2 %), hiring of competent study personnel (20.2 %), and getting funding (19.2 %). On average, investigators reported 2.8 difficulties per project (SD 2.8, range 0 to 12). In multivariable analysis, the number of difficulties was higher for studies initiated by public sponsors (vs. private), single center studies (vs. multicenter), and studies about treatment, diagnosis or prognosis (i.e., clinical vs. other studies). CONCLUSIONS: Medical researchers reported substantial logistical difficulties in conducting clinical research.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".