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Record W2497458439 · doi:10.1002/epi4.3

Not all that glitters is gold: A guide to the critical interpretation of drug trials in epilepsy

2016· review· en· W2497458439 on OpenAlexafffund
Emilio Perucca, Samuel Wiebe

Bibliographic record

VenueEpilepsia Open · 2016
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsLibin Cardiovascular Institute of AlbertaHotchkiss Brain InstituteUniversity of Calgary
FundersAgenzia Italiana del Farmaco, Ministero della SaluteCanadian Institutes of Health ResearchAlberta Innovates - Health SolutionsUniversity of Calgary
KeywordsBlindingRandomized controlled trialRandomizationConfoundingCritical appraisalExternal validityMedicineClinical study designInternal validityClinical trialPublication biasEvidence-based medicineAffect (linguistics)PsychologyClinical psychologyMeta-analysisAlternative medicineSocial psychologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Clinical trials represent the best source of evidence on which to base treatment decisions. For such evidence to be utilized meaningfully, however, it is essential that results are interpreted correctly. This requires a good understanding of strengths and weaknesses of the adopted design, the clinical relevance of the outcome measures, and the many factors that could affect such outcomes. As a general rule, uncontrolled studies tend to provide misleading evidence as a result of the impact of confounders such as regression to the mean, patient-related bias, and observer bias. On the other hand, although randomized controlled trials (RCTs) are qualitatively superior, aspects of their execution may still decrease their validity. Bias and decreased validity in RCTs may occur by chance alone (for example, treatment groups may not necessarily be balanced for important variables despite randomization) or because of specific features of the trial design. In the case of industry-driven studies, bias often influences the outcome in favor of the sponsor's product. Factors that need to be carefully scrutinized include (1) the purpose for which the trial is conducted; (2) potential bias due to unblinding or lack of blinding; (3) the appropriateness of the control group; (4) the power of the study in detecting clinically relevant differences; (5) the extent to which eligibility criteria could affect outcomes and be representative of routine clinical practice; (6) whether the treatments being compared are used optimally in terms of dosing, duration of treatment, and other variables; (7) the appropriateness of the statistical comparisons; (8) the clinical relevance of the outcome measures and whether all key outcome information is reported (for example, responder rates in completers); and (9) potential bias in the way results are presented and discussed. This article discusses each of these aspects and illustrates the discussion with examples taken from published antiepileptic drug trials.

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)0.295
metaresearch head score (Gemma)0.484
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.705
Threshold uncertainty score0.869

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2950.484
Meta-epidemiology (narrow)0.0080.005
Meta-epidemiology (broad)0.0110.008
Bibliometrics0.0190.012
Science and technology studies0.0040.030
Scholarly communication0.0190.017
Open science0.0180.012
Research integrity0.0230.044
Insufficient payload (model declined to judge)0.0110.016

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 head0.151
GPT teacher head0.490
Teacher spread0.339 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations36
Published2016
Admission routes2
Has abstractyes

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