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Record W4392850921 · doi:10.3389/jpps.2024.12797

Formulating biopharmaceuticals using three-dimensional printing

2024· article· en· W4392850921 on OpenAlexafffundvenue
Alistair K. C. Chan, Nehil Ranjitham Gopalakrishnan, Yannick Léandre Traoré, Emmanuel A. Ho

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsRegional Municipality of WaterlooNational Institute for NanotechnologyUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
Keywords3D printingFood and drug administrationThree dimensional printingDrug deliveryPharmaceutical manufacturing3d printedComputer scienceNanotechnologyRisk analysis (engineering)MedicineEngineeringPharmacologyBiomedical engineeringMaterials scienceMechanical engineering

Abstract

fetched live from OpenAlex

Additive manufacturing, commonly referred to as three-dimensional (3D) printing, has the potential to initiate a paradigm shift in the field of medicine and drug delivery. Ever since the advent of the first-ever United States Food and Drug Administration (US FDA)-approved 3D printed tablet, there has been an increased interest in the application of this technology in drug delivery and biomedical applications. 3D printing brings us one step closer to personalized medicine, hence rendering the "one size fits all" concept in drug dosing obsolete. In this review article, we focus on the recent developments in the field of modified drug delivery systems in which various types of additive manufacturing technologies are applied.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.110
GPT teacher head0.391
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2024
Admission routes3
Has abstractyes

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Same venueJournal of Pharmacy & Pharmaceutical SciencesSame topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207