WHAT SHOULD BE INCLUDED IN META-ANALYSES?
Bibliographic record
Abstract
OBJECTIVE: To explore the impact of methodologic issues on the results of meta-analyses. The following issues were examined: the type of literature search strategy used; inclusion or exclusion of non-peer-reviewed studies; the inclusion or exclusion of non-English language publications; the effect of trial quality; and the inclusion or exclusion of non-placebo-controlled studies. METHODS: The International Study of Perioperative Transfusion (ISPOT) meta-analyses were used to evaluate each of the methodologic issues. The 10 meta-analyses consisted of technologies to reduce the need for perioperative red blood cell transfusion. The number of trials for each of the meta-analyses varied from 2 to 45. Both EMBASE and MEDLINE searches were conducted, including the use of systematic search strategies. RESULTS: MEDLINE identified the vast majority of trials. Alone, MEDLINE would have missed 8 studies compared to 10 for EMBASE. Use of the systematic search strategies greatly reduced the number of articles to be reviewed compared to open searches. Type of publication, country of study origin, inclusion of non-English publications, and trial quality had very little impact on the estimates of effect. The use of placebo versus open-label control affected the magnitude of the odds ratio for two of the meta-analyses. The results of the two meta-analyses were not statistically significant if only placebo-controlled trials were included. CONCLUSIONS: While methodologic issues had very little impact on the ISPOT meta-analyses, further studies are needed in a variety of other clinical settings. Because MEDLINE, coupled with a review of the references in the identified trials, identified the vast majority of trials, one needs to consider the costs and benefits of searching EMBASE and the pursuance of unpublished and unindexed trials.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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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; a candidate call from one teacher head, not a consensus.
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".