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Record W2116589936

Practical tips for surgical research: blinding: who, what, when, why, how?

2010· article· en· W2116589936 on OpenAlexaff
Paul J. Karanicolas, Forough Farrokhyar, Mohit Bhandari

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsWestern University
Fundersnot available
KeywordsBlindingMedicineRandomized controlled trialRandomizationClinical trialDifferential treatmentIntensive care medicineMedical physicsSurgeryPathology
DOInot available

Abstract

fetched live from OpenAlex

Blinding refers to the concealment of group allocation from one or more individuals involved in a clinical research study, most commonly a randomized controlled trial (RCT). Although randomization minimizes differences between treatment groups at the outset of the trial, it does nothing to prevent differential treatment of the groups later in the trial or the differential assessment of outcomes, either of which may result in biased estimates of treatment effects. The optimal strategy to minimize the likelihood of differential treatment or assessments of outcomes is to blind as many individuals as possible in a trial. Randomized controlled trials of surgical interventions are frequently more difficult to blind than RCTs of medications, which typically achieve blinding with placebos. However, imaginative techniques may make blinding more feasible in surgical trials than is commonly believed by many researchers. In this article we discuss the importance of blinding and provide practical suggestions to researchers who wish to incorporate blinding into their surgical studies.

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)0.325
metaresearch head score (Gemma)0.222
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3250.222
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0080.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.944
GPT teacher head0.611
Teacher spread0.333 · 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; both teacher heads agree on what is shown here.

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

Citations313
Published2010
Admission routes1
Has abstractyes

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