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Record W1601129068 · doi:10.5772/38202

Innovating Medical Knowledge: Understanding Evidence-Based Medicine as a Socio-Medical Phenomenon

2012· book-chapter· en· W1601129068 on OpenAlexaff
Jorge Maya

Bibliographic record

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPhenomenonMedical knowledgePsychologyKnowledge managementMedicineEpistemologyMedical educationComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Evidence Based Medicine -Closer to Patients or Scientists?12 "Evidence based medicine," one chemist said to me, "What other kind of medicine could there possibly be?" and a consultant physician said gruffly: "We have always practiced evidence based medicine" (Hope, 1995). 1 The EBM pioneers equivocated on the movement's innovation and conservatism.It was described as both a "new paradigm" (EBMWG, 1992) and a historically-supported approach "whose philosophical origins extend back to mid-19th century Paris and earlier" (Sackett et al., 1996b).Yet it will be demonstrated in this chapter that although EBM is not best understood as a new "paradigm" or a radical departure from biomedicine, it offers methodological innovation that has shifted how we pursue, collect, and evaluate medical knowledge.Beginning with a historical account of the origins of EBM, a focus on three key methodological innovations employed by EBM will be used to advance the argument that EBM's original contribution to medicine, or what separates EBM from other approaches, is the priority it gives to certain forms of evidence, specifically evidence from randomized controlled trials.EBM offers a shift in the sort of evidence that is most highly valued for diagnosis, therapy, and prognosis questions, as heavy emphasis is placed on experimental controls and quantified measures, thus diminishing the previous status of clinical experience and observational studies significantly.This commitment represents not only methodological change, but also a novel regard of the reliability of various forms of medical knowledge.EBM offers a new answer to medicine's fundamental normative question: how ought we to practice medicine? How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following: Maya J. Goldenberg (2012).Innovating Medical Knowledge: Understanding Evidence-Based Medicine as a Socio-Medical Phenomenon, Evidence Based Medicine -Closer to Patients or Scientists?, Prof. Nikolaos Sitaras (Ed.),

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)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0030.051
Scholarly communication0.0200.028
Open science0.0030.007
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0060.001

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 head0.293
GPT teacher head0.478
Teacher spread0.185 · 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.

Study designTheoretical or conceptual
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

Citations4
Published2012
Admission routes1
Has abstractyes

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