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Record W1960922507 · doi:10.1111/bjd.14267

Biosimilars for psoriasis: preclinical analytical assessment to determine similarity

2015· review· en· W1960922507 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

VenueBritish Journal of Dermatology · 2015
Typereview
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsDermatrials Research
FundersAbbVieWyethCilagAmgen
KeywordsBiosimilarPsoriasisMedicineQuality (philosophy)Risk analysis (engineering)Product (mathematics)Similarity (geometry)Biological drugsPharmacologyIntensive care medicineBusinessComputer scienceImmunologyDiseaseArtificial intelligenceInternal medicine

Abstract

fetched live from OpenAlex

Biosimilars, sometimes called 'generic biologics', are no longer a vision for the future but a present-day reality. Drug manufacturers and regulatory authorities are charged with ensuring that these products are safe and effective. Because biologically produced medications are large, complex proteins, many factors affect the quality of the end product, including glycosylation and presence of impurities, and thus many factors need to be compared between an emerging biosimilar and its originator biologic. Indeed, preclinical analytical assessments to determine similarity to an originator biologic are critical and are considered to be the foundation for regulatory approval of biosimilars. Here, the science behind the preclinical development of biosimilars is discussed by members of the International Psoriasis Council, and suggestions are put forth to try to ensure that future biosimilars are produced in a high quality and standardized manner.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
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 head0.155
GPT teacher head0.451
Teacher spread0.296 · 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