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Distinguishing ASH clinical practice guidelines from other forms of ASH clinical advice

2024· article· en· W4394594448 on OpenAlexaff
Adam Cuker, Robert A. Kunkle, Rachel S. Bercovitz, Michael Byrne, Benjamin Djulbegović, Sandra L. Haberichter, Jennifer Holter‐Chakrabarty, Richard Lottenberg, Menaka Pai, Suely Meireles Rezende, Matthew D. Seftel, Roy L. Silverstein, Deirdra R. Terrell, Matthew C. Cheung

Bibliographic record

VenueBlood Advances · 2024
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsSunnybrook Health Science CentreUniversity of British ColumbiaUniversity of TorontoHealth Sciences CentreCanadian Blood ServicesMcMaster University
Fundersnot available
KeywordsAdvice (programming)TerminologyConcordanceTransparency (behavior)Clinical PracticeMedicineVariety (cybernetics)UnderpinningMedical educationFamily medicinePolitical scienceInternal medicineComputer scienceEngineeringLaw

Abstract

fetched live from OpenAlex

ABSTRACT: The American Society of Hematology (ASH) develops a variety of resources that provide guidance to clinicians on the diagnosis and management of blood diseases. These resources include clinical practice guidelines (CPGs) and other forms of clinical advice. Although both ASH CPGs and other forms of clinical advice provide recommendations, they differ with respect to the methods underpinning their development, the principal type of recommendations they offer, their transparency and concordance with published evidence, and the time and resources required for their development. It is crucial that end users be aware of the differences between CPGs and other forms of clinical advice and that producers and publishers of these resources use clear and unambiguous terminology to facilitate their distinction. The objective of this article is to highlight the similarities and differences between ASH CPGs and other forms of ASH clinical advice and discuss the implications of these differences for end users.

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)0.007
metaresearch head score (Gemma)0.145
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.145
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.287
GPT teacher head0.596
Teacher spread0.309 · 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.

Study designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

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