MétaCan
Menu
Back to cohort
Record W2802748326 · doi:10.1097/hs9.0000000000000044

Clinical Utility of Next‐generation Sequencing in the Management of Myeloproliferative Neoplasms: A Single‐Center Experience

2018· article· en· W2802748326 on OpenAlexaff
Waleed Alduaij, Caroline McNamara, Andre C. Schuh, Andrea Arruda, Mahadeo A. Sukhai, Nisha Kanwar, Mariam Thomas, Jay Y. Spiegel, Theodore A. Kennedy, Tracy Stockley, Hubert Tsui, Rebecca Devlin, Hassan Sibai, Dawn Maze, Aaron D. Schimmer, Karen Yee, Steven M. Chan, Suzanne Kamel‐Reid, Vikas Gupta

Bibliographic record

VenueHemaSphere · 2018
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
FundersEuropean Hematology AssociationBristol-Myers Squibb
KeywordsMedicineMyeloidOncologyInternal medicineMyelofibrosisDNA sequencingBioinformaticsGeneBiologyGeneticsBone marrow

Abstract

fetched live from OpenAlex

(7%). Some of these patients can be potential candidates for future targeted therapy trials. In conclusion, we have demonstrated that TAR-SEQ improves the characterization of TN MPN, can be integrated in clinical practice as an additional tool to refine decision making in HCT, and has the potential to identify candidates for future targeted therapy trials.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.171
GPT teacher head0.369
Teacher spread0.199 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations31
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueHemaSphereSame topicMyeloproliferative Neoplasms: Diagnosis and TreatmentFrench-language works237,207