‘Sterility Testing of Blood Components and Advanced Therapy Medicinal Products’ (Munich, April 29, 2010) Organized by the DGTI Section ‘Safety in Hemotherapy’ – Meeting Report
Bibliographic record
Abstract
Neither screening method completely detects all clinically relevant bacterial contaminations. The effect of sampling time and volume as well as standardization of the assay applied has also to be taken into account. Therefore, minimizing the risk of contamination during manufacture by measures such as donor selection, skin disinfection, division, and processing within closed systems remains crucial. In this context new concepts in sterility testing, especially with instable advanced therapy medicinal products (ATMPs), are needed as well as reassessment of pathogen inactivation techniques. At present hemovigilance data indicate that shortening the shelf life of platelet concentrates as introduced in Germany 2008 reduced the risk of transfusion-transmitted bacterial infections to the same extent as bacterial screening as done in Canada or the Netherlands. The evolving methodological progress, e.g. by standardizing culture methods or enhancing detection systems, requires careful follow-up in parallel to hemovigilance data in order to ensure optimal bacterial safety in hemotherapy.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".