Including conference abstracts rarely changed systematic review conclusions: a case study from a living network meta-analysis of COVID-19 treatments
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Including conference abstracts (CAs) in systematic reviews (SRs) helps reduce publication bias but raises concerns about reporting quality and reliability. While discrepancies with full publications are known, excluding CAs may overlook relevant early evidence. To evaluate the reporting quality of CAs, their consistency with full-text publications, and the impact of including them on effect estimates and the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) framework in a living systematic review and network meta-analysis (SRNMA) of COVID-19 drug treatments. STUDY DESIGN AND SETTING: We conducted a retrospective methodological study of all CAs included in the COVID-19 SRNMA until May 19, 2024. We assessed trial characteristics, reporting quality using Consolidated Standards of Reporting Trials (CONSORT)-A, and consistency with full-text publications. We also compared meta-analyses with and without CAs at predefined time points for mortality and hospital length of stay, evaluating changes in effect and GRADE domains. RESULTS: We included 105 CAs; 53% (56/105) were linked to a full publication. Only 7% met high reporting standards. Average consistency with full-text publications across key methodological items was 67.6%, often due to missing details in both sources. CAs enabled meta-analyses that would not have been possible at 14% of time points. Their inclusion did not affect conclusions when using the null threshold but changed the effect estimate in 55.6% and imprecision ratings in 16% of cases when using minimally important differences (MIDs). In a few instances, CAs also influenced risk of bias and inconsistency assessments. CONCLUSION: CAs can fill evidence gaps when data are limited or emerging. Although they rarely change conclusions based on the null threshold, their inclusion has a greater impact when using MID. Reviewers should assess their inclusion case by case and promote better reporting practices to enhance their contribution to SRs.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.385 | 0.778 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".