How to Improve Accrual to Clinical Trials of Symptom Control 1: Recruitment Strategies
Bibliographic record
Abstract
Inadequate patient accrual remains the primary problem for clinical trials of integrative therapies for symptom control. Many difficulties can be predicted and avoided if a careful, evidence-based approach to trial design is taken: trialists should attempt to get as many data as possible on the study population by querying institutional databases, examining case notes, following inpatient rounds, and conducting "dry runs" by asking doctors for referrals. Trials require aggressive recruitment strategies, including advertising, writing to patients at home, scanning clinic lists, and identifying critical points during clinical care at which patients can be approached. The information given to patients during any initial contact should be as simple and general as possible: presenting too much information too soon can be overwhelming and off-putting.
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 | 0.610 | 0.725 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.019 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".