Abstracts from the 1st JoPPP Conference on Pharmaceutical Policy and Practice
Bibliographic record
Abstract
Pharmaceutical industry is one of the main contributors of public health.In a pharmaceutical industry, Data integrity is one of the most important aspect that relates to drug quality, safety, efficacy and purity.Recently, many industries have been given warning letters due to the violation of data integrity and strict action had been taken by international and local regulatory authorities.Data integrity should be a part of pharmaceutical policy.It claims that product has been manufactured after meeting the predetermined specification and quality attributes and showed compliance of testing methods according to the guidelines given in official books.The FDA, USA considers integrity of data, from initial step where it is generated, and extending throughout its life cycle, to be a critical component which ensures that only high quality and safe drugs are manufactured.Hence, it is essential to record each and every detail of drug manufacturing and testing which should comply with cGMP practices.In future, the completeness, consistency, and accuracy of the data will decide the fate of the pharmaceutical companies.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.149 | 0.059 |
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".