MétaCan
Menu
Back to cohort
Record W2000978705 · doi:10.1097/brs.0b013e31816233b5

Examining Heterogeneity in Meta-Analysis

2008· review· en· W2000978705 on OpenAlexaff
Andrea D Furlan, George Tomlinson, Alejandro R. Jadad, Claire Bombardier

Bibliographic record

VenueSpine · 2008
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsInstitute for Work & HealthUniversity of Toronto
Fundersnot available
KeywordsMedicineRandomized controlled trialMeta-analysisCochrane LibraryPsychological interventionSubgroup analysisMEDLINEPhysical therapyOdds ratioStudy heterogeneityPublication biasSurgeryInternal medicine

Abstract

fetched live from OpenAlex

STUDY DESIGN: Literature review. OBJECTIVE: To assess the influence of various factors in statistical heterogeneity of meta-analyses of interventions for low back pain. One of these factors was study design: randomized controlled trial (RCT) versus nonrandomized study (NRS). SUMMARY OF BACKGROUND DATA: The presence of statistical heterogeneity poses a challenge to the conduct and interpretation of meta-analyses. METHODS: We searched MEDLINE, EMBASE, and The Cochrane Library up to May 2005 for comparative studies of interventions for low back pain. The interventions with the highest number of NRSs were selected. All NRSs and RCTs of the same interventions were combined using meta-analysis. Subgroup analyses and meta-regression were performed according to study design and other factors that were selected by a panel of 20 experts. RESULTS: NRSs frequently either agree with RCTs or underestimate the effects compared with RCTs. The interventions and the respective factors that explained statistical heterogeneity were a) surgery versus conservative treatments (17 NRSs and 8 RCTs): study design (odds ratio, OR: 1.56 and 4.69 for nonrandomized and randomized studies, respectively), pain duration (OR: 1.75 and 3.55 for chronic and acute, respectively), and involvement of workers' compensation (OR: 1.85 and 5.07, with and without, respectively); b) surgery with fusion versus surgery without fusion (17 NRSs and 3 RCTs): spondylolisthesis (OR: 2.15 and 1.22, with and without, respectively); c) Instrumented fusion versus noninstrumented fusion (15 NRSs and 8 RCTs): previous surgery (OR: 2.89 and 1.36, with and without, respectively) and levels fused (OR: 1.50 and 2.98, single and multilevel, respectively). CONCLUSION: Comparisons between RCTs and NRSs may be influenced by various factors, including study design. However, other factors were more powerful explanatory variables than study design. These factors included pain duration, involvement of workers' compensation, presence of spondylolisthesis, previous surgery, and levels fused.

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)0.138
metaresearch head score (Gemma)0.338
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.862
Threshold uncertainty score0.730

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1380.338
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0290.055
Bibliometrics0.0240.017
Science and technology studies0.0010.002
Scholarly communication0.0050.004
Open science0.0060.004
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.953
GPT teacher head0.615
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
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

Citations37
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueSpineSame topicMeta-analysis and systematic reviewsFrench-language works237,207