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Record W2188272109 · doi:10.2177/jsci.38.362b

[no title]

2015· article· en· W2188272109 on OpenAlexaff

Bibliographic record

VenueJapanese Journal of Clinical Immunology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topic14-3-3 protein interactions
Canadian institutionsKinexus Bioinformatics Corporation (Canada)
Fundersnot available
KeywordsTocilizumabMedicineAdalimumabInternal medicineGastroenterologyNuclear medicineRheumatoid arthritis

Abstract

fetched live from OpenAlex

【目的】14-3-3ηは200以上の細胞内タンパクに関係する細胞内シャペロンである,関節リウマチ(RA)では関節液および血清中にも出現する.そこで14-3-3ηに注目し,RA治療開始後の臨床的アウトカムとの関係を検討した.【成績】当科で治療導入したRA患者99(adalimumab; ADA 49,tocilizumab; TCZ 50)例で,Baseline(BL)と1年後の血清14-3-3ηをELISA法で測定し,BLと1年後の種々の臨床的指標との関係を検討した.【結果】背景中央値は年齢63歳,罹病期間76ヶ月,DAS28ESR 5.3,RF陽性82%,ACPA陽性90%で,ADA群とTCZ群で罹病期間,MTX併用率,Sharp-vdH score(SHS)で差があった.BLにおける14-3-3η濃度は,BLの疾患活動性(DAS28, CDAI, SDAI),急性炎症反応(CRP, ESR),関節破壊(SHS),血清反応(RF濃度,ACPA濃度)のいずれとも有意に相関した.また治療によりBL 0.70 ng/mlから1年後0.37 ng/mlにに低下した(p < 0.0001).1年後のDAS28-ESR寛解(η 2.6)に対するBL 14-3-3ηカットオフ値(0.32 ng/ml)をROC解析により設定し層別化すると,1年後の寛解達成率はADAではBL 14-3-3η低値群40%(8/12),高値群59%(12/29)であった(p = 0.20)が,TCZでは低値群77%(10/13),高値群37%(10/27)とBL14-3-3η低値群で有意(p = 0.0007)に高かった.【結論】BL 14-3-3η低値の場合,tocilizumabによる1年後の臨床的寛解を達成しやすいこと示された.

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)0.004
metaresearch head score (Gemma)0.015
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 score0.477
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.085
GPT teacher head0.418
Teacher spread0.333 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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