MétaCan
Menu
← Back to cohort
Record W1776324357

EVIDENCE-BASED MEDICINE: WHAT DOES THE FUTURE HOLD?

2014· article· en· W1776324357 on OpenAlexaboutno aff
Allen F. Shaughnessy

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMedical journalAlternative medicineEngineering ethicsFamily medicinePathologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

The concepts of evidence-based medicine are coming up on their thirty-fourth anniversary, born in Canada with the publication of a series of articles in the Canadian Medical Association Journal called, Clinical Epidemiology Rounds.1[i] Over this time, the ideas have slowly permeated their way into academia and are slowly diffusing into everyday clinical practice. The process of evidence-based medicine consists of five steps: 1) defining the question; 2) find evidence; 3) critically appraise this evidence; 4) apply the evidence, making a decision regarding the initial question; and, 5) monitoring one’s own practice. This process makes sense. It is also difficult. Just realizing that one has a question – the start of the process – is not easy. Finding the evidence, especially with limited computer support, can stop the process before it starts. Critically appraising evidence is a slow, difficult process for which many of us are ill equipped to do. Even if we have the skills, the time it takes to evaluate original research makes this step impractical when an answer is needed during the care of patients. Problems remain when the answer is obtained. If I do something based on this evidence, will my patient be better off as a result? There are many examples in medicine of when patients were inadvertently harmed because of interventions that seemed to make sense but worsened clinical outcomes. One of the most infamous examples is the treating of asymptomatic premature ventricular contractions following a myocardial infarction, which resulted in an average increase in mortality.[ii] Many other examples of “doing the wrong thing for the right reasons” exist in medicine’s history. [i]. [No author listed]. Department of clinical epidemiology and How to read clinical journal: I. Why to read them and how to start reading them critically. Can Med Assoc J. 1981 Mar 1;124(5):555-8. [ii]. Echt DS, Liebson PR, Mitchell LB, et al. Mortality and morbidity in patients receiving encainide, flecainide, or placebo. The Cardiac Arrhythmia Suppression Trial. N Engl J Med. 1991 Aug 22;325(8):584-5. N Engl J Med 1991 Mar 21;324(12):781-8.

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)0.134
metaresearch head score (Gemma)0.206
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.866
Threshold uncertainty score0.710

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1340.206
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0080.007
Science and technology studies0.0070.033
Scholarly communication0.0300.052
Open science0.0080.008
Research integrity0.0300.049
Insufficient payload (model declined to judge)0.0170.006

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 head0.588
GPT teacher head0.689
Teacher spread0.101 · 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 designNot applicable
DomainMethods
GenreCommentary

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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)→Same topicHealth Sciences Research and Education→French-language works237,207→