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Quality Analysis of the GI Literature on Endoscopic Ultrasound

2009· article· en· W2977578259 on OpenAlexaff
Lloyd R. Sutherland, Monica Cepoiu‐Martin, Mahnaz Youssefi, Diane Lorenzetti, Carla Nash

Bibliographic record

VenueThe American Journal of Gastroenterology · 2009
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineBlindingConsolidated Standards of Reporting TrialsChecklistGeneralizability theoryRandomized controlled trialRandomizationData extractionMedical physicsSample size determinationSystematic reviewMEDLINEFamily medicineSurgeryStatistics

Abstract

fetched live from OpenAlex

Purpose: To describe the quality of randomized controlled trials (RCTs) and meta-analyses (MAs) reporting on gastro-intestinal (GI) indications of endoscopic ultrasound (EUS) using two validated quality assessment tools. Methods: We searched several databases for papers on GI indications of EUS. Our search was limited to RCT and MA published in English between 2004 and 2009. Two independent reviewers analyzed the quality of reporting using the CONSORT Statement Checklist (RCTs) and the AMSTAR measurement tool (MAs). Discrepancies between the assessments were discussed and resolved by consensus. Results: We reviewed 9 RCTs comparing EUS to other visualization techniques (ERCP, EGD, CT) for the treatment or detection of various GI ailments. Only one report followed the CONSORT Statement Checklist. One report failed to indicate the eligibility criteria for participants, 3 reports did not state the specific objectives and hypotheses of the study, 4 reports did not list clearly defined primary and secondary outcome measures, one report did not indicate how the sample size was determined, 6 reports failed to included details on randomization methods and implementation, 6 reports did not discuss blinding issues, one report did not include baseline demographic and clinical data of the sample, 7 reports did not address multiplicity, one report did not stated the adverse events, and 4 reports did not discuss the generalizability of the trial findings. Among the 10 MAs reviewed, 3 did not report duplicate study selection and data extraction, 3 did not report a comprehensive literature search, 6 did not include an appropriate summary of the characteristics of the included studies, 4 did not include the quality assessment of the included studies, one did not use appropriate methods to combine the results and 4 did not assess the publication bias. None of the 10 MAs used the AMSTAR tool used the scientific quality of the included studies in formulating their conclusion, even if the quality assessment was reported. Conclusion: The quality of RCT reports is an important selection criterion for inclusion in meta-analyses. Despite the fact at least 2 of the 3 journals where these articles were published included the CONSORT Statement among their criteria for accepting a study, there are many gaps in the reporting of RCTs. Moreover, most of the MAs reviewed failed to meet several AMSTAR quality criteria, which may affect the validity of their conclusions.

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)0.369
metaresearch head score (Gemma)0.710
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.631
Threshold uncertainty score0.778

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3690.710
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.025
Bibliometrics0.0360.030
Science and technology studies0.0020.004
Scholarly communication0.0100.006
Open science0.0060.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.298
Teacher spread0.286 · 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 designObservational
DomainEvaluation
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

Citations0
Published2009
Admission routes1
Has abstractyes

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