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Record W2111500110 · doi:10.1248/cpb.50.1155

Influence of Physical Parameters and Lubricants on the Compaction Properties of Granulated and Non-granulated Cross-linked High Amylose Starch.

2002· article· en· W2111500110 on OpenAlexaff
Miloud Rahmouni, Vincent Lenaerts, Danielle Massuelle, Éric Doelker, A. Leroux

Bibliographic record

VenueChemical and Pharmaceutical Bulletin · 2002
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMagnesium stearateTabletingGranulationExcipientCompactionChemistryParticle sizeStarchCompressibilityComposite materialChemical engineeringMaterials scienceDosage formChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Cross-linked high amylose starch (CLA) is a pharmaceutical excipient used in direct compression for the preparation of controlled release tablets and implants. In this work the compression properties of CLA in bulk and granulated forms (without binder) were evaluated for the first time. Tablets were prepared on an instrumented single punch machine. The flow properties and the compression characteristics (compressibility, densification behavior, work of compression) of the materials as well as the mechanical strength of the finished compacts (compactibility) were systematically examined. Wet granulation was found to improve the flowability and the compressibility of CLA but concomitantly reduced its compactibility. It was demonstrated that CLA was a plastically deforming material with a plasticity index and a yield pressure value comparable to those of pregelatinized starch. The compactibility of granulated CLA was independent of particle size in the range of 75 to 500 microm, but slightly decreased when the percentage of the fine particles (<75 microm) in the bulk powder was increased. Water and colloidal silicone dioxide facilitated the consolidation of CLA, while magnesium stearate had an opposite effect on the tablet crushing force.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.281
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2002
Admission routes1
Has abstractyes

Explore more

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