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Record W2899435684 · doi:10.3747/co.25.4092

Canadian Evidence-Based Guideline for the First-Line Treatment of Chronic Lymphocytic Leukemia

2018· article· en· W2899435684 on OpenAlexafffundvenueabout
Carolyn Owen, Alina S. Gerrie, Versha Banerji, Sarit Assouline, Christine Chen, Katherine Robinson, E. Lye, Graeme Fraser

Bibliographic record

VenueCurrent Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsMcMaster UniversityJuravinski Cancer CentreLeukemia & Lymphoma Society of CanadaQueen Elizabeth II Health Sciences CentreDalhousie UniversityUniversity of TorontoMcGill UniversityUniversity of British ColumbiaJewish General HospitalCancerCare ManitobaPrincess Margaret Cancer CentreBC Cancer AgencyFoothills Medical Centre
FundersH. Lundbeck A/SCancerCare Manitoba FoundationAstraZenecaCelgeneGilead SciencesResearch ManitobaPfizer
KeywordsGuidelineChronic lymphocytic leukemiaMedicineFront lineFamily medicineFirst line treatmentLeukemiaIntensive care medicineImmunologyPathologyInternal medicineChemotherapyPolitical science

Abstract

fetched live from OpenAlex

Chronic lymphocytic leukemia (cll) is the most common adult leukemia in North America. In Canada, no unified national guideline exists for the front-line treatment of cll; provincial guidelines vary and are largely based on funding. A group of clinical experts from across Canada developed a national evidence-based treatment guideline to provide health care professionals with clear guidance on the first-line management of cll. Consensus recommendations based on available evidence are presented for the first-line treatment of cll.

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)0.005
metaresearch head score (Gemma)0.020
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: Methods · Consensus signal: none
Teacher disagreement score0.421
Threshold uncertainty score0.848

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0060.007
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0050.001
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0110.004

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 head0.204
GPT teacher head0.456
Teacher spread0.252 · 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
GenreMethods

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

Citations12
Published2018
Admission routes4
Has abstractyes

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