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Record W2080520080 · doi:10.1353/pbm.0.0079

Making the Grade: Assuring Trustworthiness in Evidence

2009· article· en· W2080520080 on OpenAlexaff
Ross Upshur

Bibliographic record

VenuePerspectives in biology and medicine · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCredibilityNormativeContext (archaeology)Evidence-based medicineInterpretation (philosophy)Scientific evidenceSoundnessTrustworthinessHealth careEngineering ethicsEpistemologyRelevance (law)Articulation (sociology)PsychologyMEDLINEPolitical scienceSocial psychologyLawComputer scienceEngineeringPhilosophy

Abstract

fetched live from OpenAlex

Despite evidence-based medicine's (EBM's) significant evolution and maturation from its revolutionary origins to its current form as the preeminent means of practicing medicine, there are still good reasons to be unsatisfied with EBM. This essay explores two important new developments in EBM: recently articulated accounts of the scientific basis of EBM, and the related writings of the GRADE Working Group to create standards for interpretation of the medical literature and evaluation of recommendations. A review of Karanicolas, Kunz, and Guyatt's (2008) three-step articulation of EBM's scientific basis demonstrates that the supposed soundness of each principle is not attributable to its scientific status; instead, the normative language of each principle highlights EBM's grounding in an only partially articulated philosophical framework. The GRADE Working Group's effort similarly relies on credibility, consensus, and trust in its defense and justification of EBM. These recent developments in EBM reveal that if the clinical research literature is to be informative or foundational to the enterprise of health care, much work needs to be done to secure its trustworthiness and integrity. An agenda for examining trust and trustworthiness in the context of health research is proposed.

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)0.006
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.733
GPT teacher head0.658
Teacher spread0.074 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations24
Published2009
Admission routes1
Has abstractyes

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