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Record W1805646557 · doi:10.1186/1471-2288-5-8

Evaluating the role of quality assessment of primary studies in systematic reviews of cancer practice guidelines

2005· article· en· W1805646557 on OpenAlexafffund
Melissa Brouwers, Mary Johnston, Manya Charette, Steven Hanna, Alejandro R. Jadad, George P. Browman

Bibliographic record

VenueBMC Medical Research Methodology · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity Health NetworkHamilton Regional Laboratory Medicine ProgramCancer Care OntarioUniversity of TorontoMcMaster University
FundersOntario Ministry of Health and Long-Term CareCancer Care Ontario
KeywordsSystematic reviewQuality (philosophy)MedicineMEDLINEQuality of evidenceQuality assessmentPrimary carePsychologyFamily medicineMeta-analysisPolitical scienceExternal quality assessmentPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to evaluate the role of study quality assessment of primary studies in cancer practice guidelines. METHODS: Reliable and valid study quality assessment scales were sought and applied to published reports of trials included in systematic reviews of cancer guidelines. Sensitivity analyses were performed to evaluate the relationship between quality scores and pooled odds ratios (OR) for mortality and need for blood transfusion. RESULTS: Results found that that whether trials were classified as high or low quality depended on the scale used to assess them. Although the results of the sensitivity analyses found some variation in the ORs observed, the confidence intervals (CIs) of the pooled effects from each of the analyses of high quality trials overlapped with the CI of the pooled odds of all trials. Quality score was not predictive of pooled ORs studied here. CONCLUSIONS: Had sensitivity analyses based on study quality been conducted prospectively, it is highly unlikely that different conclusions would have been found or that different clinical recommendations would have emerged in the guidelines.

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)0.941
metaresearch head score (Gemma)0.983
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.815
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.9410.983
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0100.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.996
GPT teacher head0.862
Teacher spread0.133 · 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 designOther design
DomainMethods
GenreMethods

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
Published2005
Admission routes2
Has abstractyes

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