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Record W3001224118 · doi:10.1002/pbc.28170

Lexicon for guidance terminology in pediatric hematology/oncology: A White Paper

2020· review· en· W3001224118 on OpenAlexafffund
L. Lee Dupuis, Paula D. Robinson, Marianne D. van de Wetering, Wim J. E. Tissing, Jennifer Seelisch, Carol Digout, Lillian Sung

Bibliographic record

VenuePediatric Blood & Cancer · 2020
Typereview
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsAtlantic School of TheologyWestern UniversityChildren's Hospital of Western OntarioUniversity of TorontoPediatric Oncology GroupHospital for Sick ChildrenLondon Health Sciences Centre
FundersHospital for Sick ChildrenNational Institute for Health and Care ResearchPediatric Oncology Group of Ontario
KeywordsMedicineTerminologyLexiconHematologyPediatric oncologyInternal medicineWhite (mutation)OncologyMedical physicsIntensive care medicineNatural language processingCancerLinguisticsComputer scienceGenetics

Abstract

fetched live from OpenAlex

Terms used to label types of clinical recommendations and guidance are applied inconsistently and do not reflect the methods used to create each type. Here, the international Pediatric Oncology Supportive Care Guideline Network proposes a lexicon for types of recommendations and guidance documents. A lexicon describing three types of recommendations (clinical practice guideline-derived, good practice statement, and expert opinion statement) and two types of guidance documents (clinical practice guideline and expert opinion) is presented. Consistent use of this lexicon will allow pediatric oncology clinicians to readily appreciate the methods used to create clinical guidance.

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)0.014
metaresearch head score (Gemma)0.049
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: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0140.018
Science and technology studies0.0010.003
Scholarly communication0.0060.007
Open science0.0050.004
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0160.015

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 head0.234
GPT teacher head0.515
Teacher spread0.282 · 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

Citations16
Published2020
Admission routes2
Has abstractyes

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