U.S. Food and Drug Administration Inspections of Clinical Investigators: Overview of Results from 1977 to 2009
Bibliographic record
Abstract
The U.S. Food and Drug Administration (FDA) Center for Drug Evaluation and Research is responsible for evaluating drug safety and efficacy, including oversight of clinical trials and principal investigators. The FDA Clinical Investigator Inspection List (CIIL) contains online, detailed, relevant information of all FDA inspections. We reviewed FDA inspections of clinical investigators to ascertain their outcome and included all inspections on the list (July 1977 through December 31, 2009; n = 9,481 inspections). Eighty-eight percent of inspections were "data audit" (primary purpose = verification of data), and the rest (12%) were "for cause." The number of inspections each year significantly increased over time (P < 0.0001) and averaged 350 per year in the past decade. No deficiencies were found in only 11.2% of all "data audit" and 5% of all "for cause" inspections. Only 31% of inspections resulted in "no action indicated." About two thirds of inspections resulted in some finding, requiring either voluntary investigator action (61.3% of inspections) or official FDA action (3.9%). The most frequently cited deficiencies were failure to follow investigational plan (34%), inadequate informed consent form (28%), and inadequate/inaccurate records (27%). In conclusion, over the past decade, the FDA has performed approximately 350 inspections per year, with the number increasing over time. The vast majority of FDA inspections yield deficiency findings and, as a result, only about one third of inspections have an outcome of "no action indicated."
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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 | 0.030 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".