Insights into tablet sticking: a quantitative case study with an ibuprofen and methocarbamol-based formulation
Bibliographic record
Abstract
Objectives and Methods Tablet sticking is a continuous accumulation of pharmaceutical powder onto tooling surfaces during compression. Its occurrence greatly impacts tablet productivity, quality attributes, and tooling age. In a previous study, the authors proposed a multivariate data analysis approach to gain insights into tablet sticking directly on the industrial stage. The objective was to determine the combination of factors that could help distinguish between batches affected and unaffected by sticking. The present study aims to generalize this approach by extending it to quantitative predictions of punch sticking intensity. A total of 345 variables was gathered on 28 industrial batches of an ibuprofen and methocarbamol-based formulation.Result and Conclusion Using PLS regression models, it was shown that the association of granulation duration and compression force allows to significantly explain ∼60% of sticking variations of studied formulation. In addition, unlike the classification models developed in the earlier work, the validation residues in the present study were found to be normally distributed (Shapiro–Wilks p value = 0.96) and independent from the target variable (R2 = 9.5%).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".