Assessing the Methodological Quality of Nonrandomized Intervention Studies
Bibliographic record
Abstract
In many areas of health care, randomized controlled trials (the best evidence regarding the effectiveness of health care interventions) are lacking and decision-makers have to rely on evidence from nonrandomized studies (NRS). We conducted a Medline search to identify English-language articles describing instruments for assessing the quality of NRS of health care interventions. These instruments varied greatly in scope, in the number and types of items and in developmental rigor. Items commonly included were those related to specification of study questions, allocation method, comparability of groups, and blinding of outcome assessment. We do not support the development of a generic scale to evaluate the methodological quality of nonrandomized intervention studies. Instead, further study should be directed to investigate the degree to which, and the circumstances under which, different methodological characteristics are associated with bias. This information will assist researchers in identifying a priori which methodological characteristics need careful evaluation in particular studies.
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 | 0.736 | 0.872 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".