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Record W2204309863

User's guide to the orthopaedic literature: how to use a systematic literature review.

2002· review· en· W2204309863 on OpenAlexaff
Mohit Bhandari, Gordon Guyatt, Víctor M. Montori, P.J. Devereaux, M.F. Swiontkowski

Bibliographic record

VenuePubMed · 2002
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSystematic reviewComputer sciencePolitical scienceMEDLINELaw
DOInot available

Abstract

fetched live from OpenAlex

• Investigators who perform a systematic review address a focused clinical question, conduct a thorough search of the literature, apply inclusion and exclusion criteria to each potentially eligible study, critically appraise the relevant studies, conduct sensitivity analyses, and synthesize the information to draw conclusions relevant to patient care or additional study. • A meta-analysis is a quantitative (or statistical) pooling of results across eligible studies with the aim of increasing the precision of the final estimates by increasing the sample size. • The current increase in the number of small randomized trials in orthopaedic surgery provides a strong argument in favor of meta-analysis; however, the quality of the primary studies included ultimately reflects the quality of the pooled data from a meta-analysis. The conduct and publication of systematic reviews of the orthopaedic literature, which often include statistical pooling or meta-analysis, are becoming more common. This article is the third in a series of guides evaluating the validity of the surgical literature and its application to clinical practice. It provides a set of criteria for optimally interpreting systematic literature reviews and applying their results to the care of surgical patients. Authors of traditional literature reviews provide an overview of a disease or condition or one or more aspects of its etiology, diagnosis, prognosis, or management, or they summarize an area of scientific inquiry. Typically, these authors make little or no attempt to be systematic in formulating the questions that they are addressing, in searching for relevant evidence, or in summarizing the evidence that they consider. Medical students and clinicians seeking background information nevertheless often find these reviews very useful for obtaining a comprehensive overview of a clinical condition or area of inquiry. When traditional expert reviewers make recommendations, they often disagree with one another, and their advice frequently lags behind, or …

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)0.042
metaresearch head score (Gemma)0.238
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: Review · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.851

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.238
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0230.032
Science and technology studies0.0010.002
Scholarly communication0.0070.008
Open science0.0060.006
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.2540.151

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 head0.514
GPT teacher head0.460
Teacher spread0.054 · 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
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

Citations47
Published2002
Admission routes1
Has abstractyes

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