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Statistical evaluation of tablet coating processes: influence of pan design and solvent type

2010· article· en· W1834737048 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBrazilian Journal of Pharmaceutical Sciences · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicAdvanced Statistical Process Monitoring
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCoatingFactorial experimentStatistical analysisMaterials scienceSignificant differenceProcess engineeringMathematicsComposite materialEngineeringStatistics

Abstract

fetched live from OpenAlex

Partially and fully perforated pan coaters are among the most relevant types of equipment currently used in the process of coating tablets. The goal of this study was to assess the performance differences among these types of equipment employing a factorial design. This statistical approach allowed the simultaneous study of the process variables and verification of interactions among them. The study included partially-perforated and fully-perforated pan coaters, aqueous and organic solvents, as well as hypromellose-based immediate-release coating. The dependent variables were process time, energy consumption, mean weight of tablets and process yield. For the tests, placebo tablets with a mean weight of 250 mg were produced, divided into eight lots of two kilograms each and coated in duplicate, using both partially perforated pan and fully perforated pan coaters. The results showed a significant difference between the type of equipment used (partially and fully perforated pan coaters) with regard to process time and energy consumption, whereas no significant difference was identified for mean weight of the coated tablets and process yield.

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.

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)0.016
metaresearch head score (Gemma)0.060
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.060
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.230
GPT teacher head0.517
Teacher spread0.287 · 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