Practical tips for surgical research: how to optimize patient recruitment.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the most common challenges of randomized controlled trials (RCTs), both published and unpublished, is related to problems with recruitment. Investigators’ enthusiasm for ambitious recruitment in a trial often dissipates quickly with the realization that ambitious recruitment is often misguided. This common error has been dubbed “Lasagna’s Law”1 and Muench’s Third Law.2 Both laws point to the same principle: investigators greatly overestimate the pool of available patients who meet the inclusion criteria.3 Insufficient or untimely patient recruitment into RCTs has serious consequences. The length of the trial may need to be extended, leading to increased resource use and costs. Lengthy trials delay the availability of potentially beneficial treatments to the public.4 The integrity and validity of the study also rely on an adequate sample size. If the sample size is not achieved, there is an increased chance of committing a type II error (e.g., you are more likely to find no difference between treatments when one actually exists). The trial may have to be abandoned, and the results may not be publishable. The recruitment rate is influenced by both patient and investigator factors. A recent systematic review by Abraham and colleagues5 identified reasons why eligible patients may not want to participate in real or hypothetical surgical RCTs. Surgeons were also asked why they did not want to enroll eligible patients into real or hypothetical surgical trials. The top reasons for patient nonentry were that the patient had a preference for a certain therapy, he or she did not understand the trial (trial too complex), the patient did not want to be randomly assigned to a treatment and he or she feared a negative outcome or receiving a treatment that he or she felt was inferior. Investigators had similar reasons for not entering eligible patients, including difficulty following the study protocol (trial too complex) and completing the follow-up requirements, preference for a certain therapy and difficulties obtaining informed consent from patients. Understanding and addressing potential patient and investigator concerns is important when developing a recruitment strategy. In this article, we discuss the common issues encountered in recruiting patients for surgical trials. It is intended for anyone conducting surgical trials, including medical students, residents, and junior and senior researchers. By the end of this article, readers will be able to develop strategies to avoid some of the common pitfalls in recruitment and, if these difficulties occur, to rectify them.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.201 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it