Bibliographic record
Abstract
This SAMs Therapy Educational Course will introduce participants to the tools recommended by TG 100 for use in the development of a risk‐based Quality Management Program. The Course will start with an Overview of the background and rationale behind the TG 100 initiative, from which its charge was developed. After setting the scene, four 15 minute presentations will introduce the principal components of the soon to be published Report of TG 100. These are Process Mapping, Failure Modes and Effects Analysis, Fault Tree Analysis and the development of a QA/QM risk‐based Program. There will be time for two short exercises based on Failure Modes and Effects Analysis and a Discussion before the SAMs questions which will conclude the session. Learning Objectives: To appreciate the underlying philosophy of the TG 100 initiative. To gain a brief overview of the principal risk‐based tools recommended by TG 100. In an informal workshop format, to explore, at an introductory level, the use of risk analysis as proposed by TG 100 as a prelude to the development of a Quality Management Program.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.017 |
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".