MétaCan
Menu
Back to cohort
Record W3014462878 · doi:10.4049/jimmunol.1901477

Translating JAKs to Jakinibs

2020· review· en· W3014462878 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

VenueThe Journal of Immunology · 2020
Typereview
Languageen
FieldMedicine
TopicCytokine Signaling Pathways and Interactions
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsIdentification (biology)DiseaseImmune systemBiologyComputational biologyMedicineImmunologyBioinformaticsPathology

Abstract

fetched live from OpenAlex

Abstract The discovery of JAKs and STATs and their roles in cytokine and IFN action represented a significant basic advance and a new paradigm in cell signaling. This was quickly followed by discoveries pointing to their essential functions, including identification of JAK3 mutations as a cause of SCID. This and other findings predicted the use of therapeutically targeting JAKs as a new strategy for treating immune and inflammatory diseases. This now is a reality with seven approved jakinibs being used to treat multiple forms of arthritis, inflammatory bowel disease and myeloproliferative neoplasms, and numerous ongoing clinical trials in other settings. This story provides interesting insights into the process of translating basic discoveries and also reveals the need to return to basic work to fill gaps that now become apparent.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.081
GPT teacher head0.374
Teacher spread0.294 · 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