Medical and Clinical Pathology Pre-Screening Visit and Enrolment Seasonal Variability in Healthy Volunteers
Bibliographic record
Abstract
In the pharmaceutical industry, selection of healthy volunteers is one of the foundations in phase I clinical trials and is a difficult and costly process. The objectives of this study were to evaluate the seasonal variability in recruiting healthy volunteers and examine the value of utilizing a pre-screen visit for healthy volunteers to generate a database pool that will be used in the routine screening process of phase I clinical research studies. We retrospectively studied a total of 1115 male and female volunteers who were scheduled for a medical and clinical pathology pre-screen visit over a one year period. Written consents were obtained from all individuals who participated in the study. Medical pre-screen visit included a full medical history and examination and electrocardiogram and clinical pathology (clinical chemistry, hematology and urinalysis). There was apparent seasonal variability in the participation of individuals in the pre-screen visit. Increased values of clinical chemistry values such as alanine aminotransferase and aspartate aminotransferase accounted for the majority of the clinically relevant increased values. Increased values of WBC’s and both platelets and mean corpuscular volume accounted for the lowest values. In urinalysis, the most prevalent abnormal values were increased WBC’s and red blood cells. No apparent differences were seen between sexes.Conducting medical and clinical pathology pre-screening visit is important as a source for healthy volunteer database pool to participate in phase I trials.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".