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Record W2891539948 · doi:10.1016/j.clgc.2018.09.005

Updated Recommendations on the Diagnosis, Management, and Clinical Trial Eligibility Criteria for Patients With Renal Medullary Carcinoma

2018· article· en· W2891539948 on OpenAlexaff
Pavlos Msaouel, Andrew L. Hong, Elizabeth A. Mullen, Michael B. Atkins, Cheryl L. Walker, Chung‐Han Lee, Marcus A. Carden, Giannicola Genovese, W. Marston Linehan, Priya Rao, Maria J. Merino, Howard Grodman, Jeffrey S. Dome, Conrad V. Fernandez, James I. Geller, Andrea B. Apolo, Najat C. Daw, H. Courtney Hodges, Marva Moxey‐Mims, Darmood Wei, Donald P. Bottaro, Michael Staehler, José A. Karam, W. Kimryn Rathmell, Nizar M. Tannir

Bibliographic record

VenueClinical Genitourinary Cancer · 2018
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
FundersKidney Cancer AssociationNational Cancer InstituteConquer Cancer FoundationNational Institutes of HealthCancer Prevention and Research Institute of TexasJohnson Foundation
KeywordsMedicineSickle cell traitClinical trialRenal cell carcinomaDiseaseDosingInternal medicineChemotherapyIntensive care medicineOncologyNephrectomyKidney

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)0.026
metaresearch head score (Gemma)0.086
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 score0.026
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.086
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.008
Bibliometrics0.0040.003
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0040.002
Research integrity0.0130.016
Insufficient payload (model declined to judge)0.0100.004

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 head0.134
GPT teacher head0.453
Teacher spread0.319 · 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

Citations96
Published2018
Admission routes1
Has abstractno

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