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Record W4210632919 · doi:10.1016/s2352-3026(21)00283-0

Consensus guidelines and recommendations for infection prevention in multiple myeloma: a report from the International Myeloma Working Group

2022· review· en· W4210632919 on OpenAlexaff
Noopur Raje, Elias Anaissie, Shaji Kumar, Sagar Lonial, Thomas G Martin, Morie A. Gertz, Amrita Krishnan, Parameswaran Hari, Heinz Ludwig, Elizabeth O’Donnell, Andrew J. Yee, Jonathan L. Kaufman, Adam D. Cohen, Laurent Garderet, Ashutosh F Wechalekar, Evangelos Terpos, Navin Khatry, Rubén Niesvizky, Qing Yi, Douglas Joshua, Tapan Saikia, Nelson Leung, Monika Engelhardt, Mohamad Mothy, Andrew R. Branagan, Ajai Chari, Tony Reiman, Brea Lipe, Joshua Richter, S. Vincent Rajkumar, Jesús F. San Miguel, Kenneth C. Anderson, Edward A. Stadtmauer, Rao Prabhala, Phillip McCarthy, Nikhil C. Munshi

Bibliographic record

VenueThe Lancet Haematology · 2022
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsSaint John Regional Hospital
Fundersnot available
KeywordsMedicineMultiple myelomaIntensive care medicineContext (archaeology)VaccinationDiseaseImmunologyInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.024
metaresearch head score (Gemma)0.027
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.009
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0060.004
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0050.002

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 head0.332
GPT teacher head0.457
Teacher spread0.125 · 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
GenreReview

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

Citations191
Published2022
Admission routes1
Has abstractno

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