Roller Compaction and Tabletting of St. John's Wort Plant Dry Extract Using a Gap Width and Force Controlled Roller Compactor. II. Study of Roller Compaction Variables on Granule and Tablet Properties by a 3<sup>3</sup>Factorial Design
Bibliographic record
Abstract
The purpose of this study was to investigate the influence of roller compaction parameters and the amount of magnesium stearate used in dry granulation on granule and tablet properties of a dry herbal extract from St. John's wort (Hypericum perforatum L.). Two different extract batches were blended with magnesium stearate and compacted using a gap width and force controlled roller compactor. A 3(3) factorial design was used to evaluate the influence of the three independent variables, the amount of magnesium stearate, the roller compaction force, and the granulating sieve size on the mean particle size of granulated extracts and on the disintegration time of tablets containing these granulated extracts. The evaluation was done by multilinear stepwise regression analysis. The mean particle size d50 (R2 > 0.9) of both compacted extracts increased with increasing compaction force and with granulating sieve size. The disintegration time of the tablets was mostly in the range 5-15 min and increased slightly with increasing magnesium stearate concentration in the compacted extract and with decreasing compaction force of the roller compaction. The incorporation of magnesium stearate into the granulated extract reduced its potential negative influence on the disintegration time, while maintaining its functionality as a lubricant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".