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Record W2083073031 · doi:10.1111/bjh.12985

Use of <scp>JAK</scp> inhibitors in the management of myelofibrosis: a revision of the <scp>B</scp>ritish <scp>C</scp>ommittee for <scp>S</scp>tandards in <scp>H</scp>aematology <scp>G</scp>uidelines for <scp>I</scp>nvestigation and <scp>M</scp>anagement of <scp>M</scp>yelofibrosis 2012

2014· letter· en· W2083073031 on OpenAlexaff
John T. Reilly, Mary Frances McMullin, Philip Beer, Nauman M. Butt, Eibhlin Conneally, Andrew Duncombe, Anthony R. Green, G. Mikhaeel, Maria Gilleece, Steven Knapper, Adam J. Mead, Ruben A. Mesa, Mallika Sekhar, Claire Harrison

Bibliographic record

VenueBritish Journal of Haematology · 2014
Typeletter
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
FundersMedical Research CouncilPublic Health Agency
KeywordsRuxolitinibMyelofibrosisMedicineHazard ratioInternal medicineCalreticulinConfidence intervalHematologyOncologyBiologyBone marrowGenetics

Abstract

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The British Committee for Standards in Haematology (BCSH) Guidelines for myelofibrosis were produced in 2012 (Reilly et al, 2012), but since then Ruxolitinib, a JAK1/JAK2 inhibitor, has been approved for use in the European Union and highly prevalent mutations in the Calreticulin gene (CALR) have been described. We therefore wish to revise the existing guideline (Reilly et al, 2012) to accommodate this important data. Current diagnostic criteria should be modified to incorporate testing for the CALR mutations into major criteria A2 alongside JAK2 V617F, as shown in Table 1 (Evidence grade 1A). Patients with CALR mutations may have a better prognosis (Klampfl et al, 2013), but this has not formally been assessed and incorporated into prognostic scores. Substantial data are now available concerning responses to JAK inhibitor therapies including beneficial effects upon survival (Verstovsek et al, 2012, 2013; Cervantes et al, 2013). For example, at 144 weeks in the COMFORT-II study the median of overall survival had not been reached in either arm. A total of 29 (19·9%) and 22 (30·1%) patients died during the study in the ruxolitinib and best available therapy (BAT) arms, respectively, of which deaths on treatment were reported for 13 (8·9%) in the ruxolitinib arm, and 5 (6·8%) in the BAT arm (one death occurred after crossover to ruxolitinib). There was a 52% reduction in risk of death in the ruxolitinib treatment arm compared to the BAT arm (Hazard Ratio = 0·48, 95% confidence interval 0·28–0·85). The estimated probability of being alive at 144 weeks was 81% in ruxolitinib arm and 61% in BAT arm. The P-value for the log-rank test stratified by the baseline risk category was 0·009, (Cervantes et al, 2013). Furthermore, data from these randomized studies suggest that standard therapies are comparable to placebo in terms of spleen and symptom responses. The previous guideline (Reilly et al, 2012) recommended consideration of JAK inhibitor therapy for patients who have failed hydroxycarbamide therapy and are not presently suitable for bone marrow transplantation, or for patients with severe constitutional symptoms. In view of new evidence we now formally recommend ruxolitinib as first line therapy for symptomatic splenomegaly and/or myelofibrosis-related constitutional symptoms regardless of JAK2 V617F mutation status (evidence grade 1A) where the balance between need to resolve the latter outweighs risk of side effects and, in particular, we make the following recommendations: Whilst treatment with ruxolitinib is suggested to confer a survival advantage treatment with this agent in asymptomatic patients and/or those who lack bothersome splenomegaly is not currently recommended. For patients failing or intolerant of ruxolitinib, additional JAK inhibitors are being assessed in clinical trials and may be approved in the future. The content was reviewed and approved by all authors, the manuscript was written by CH. John T. Reilly has acted as consultant or been paid on the speakers bureau for Novartis and Shire. Mary Frances McMullin has acted as a consultant or been on the speakers bureau for Novartis, Sanofi, Shire and Gilead pharmaceuticals. Philip A. Beer, none. Nauman Butt has received sponsorship to attend educational meetings from Novartis and Shire Pharmaceuticals, and acted as a speaker for educational meeting sponsored by Novartis and Bristol-Myers Squibb. Eibhlin Conneally has acted as an advisory board member for Novartis, Bristol-Myers Squibb and Pfizer Pharmaceuticals. Andrew Duncombe has acted as an advisory board or speaker bureau member for Novartis, Sanofi, Amgen, Roche and Baxter. Anthony R. Green, none. N. George Mikhaeel, none. Marie H. Gilleece, none. Steven Knapper has acted as a consultant for Novartis and has received funding for overseas conference travel from Novartis, Shire. Adam Mead has received consultancy fees from Novartis and Sanofi Aventis and research funding from Novartis. Ruben A. Mesa has received research support from Incyte, Genentech, Sanofi, Lilly, NS pharma and Gilead and consultancy fees from Novartis. Mallika Sekhar has received research funding from Novartis. Claire Harrison has received research funding from Novartis pharmaceuticals, acted as a consultant or been on the speakers bureau for Novartis, Sanofi, Shire, Celgene, YMBioscience, SBio, CTI and Gilead pharmaceuticals.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.278
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations38
Published2014
Admission routes1
Has abstractyes

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