Evaluating and Optimising the Retrieval of Research Evidence \nfor Systematic Reviews of Adverse Drug Effects and Adverse Drug Reactions \n
Bibliographic record
Abstract
Abstract \nSystematic reviews can provide timely, reliable evidence on which to make informed decisions. In order to make balanced decisions, information is not only needed on the benefits of an intervention, but also on its adverse effects. Yet few systematic reviews incorporate adverse effects data in their analysis. There is currently a lack of guidance on how to identify adverse effects data, this may impede systematic reviewers. This thesis helps address this situation by evaluating and optimising the methods for retrieval of research evidence for systematic reviews of adverse effects. \n \nThe first stage of this programme of research critically reviews the methodological literature relating to the retrieval and inclusion of adverse effects data, including aspects such as the impact of study design (for example RCTs and cohort studies), database search strategies (for example in MEDLINE and EMBASE), sources of data (including database and non-database sources), publication status and funding status. \n \nSecond, the results of a survey of the literature searching methods used in 849 systematic reviews of adverse effects are presented. Data were collated on aspects such as sources searched, search strategy design and the standard of reporting of the methods used. The reviews are published over a 17 year time period (1994-2011) thus enabling time trends analysis. The methods used in these systematic reviews of adverse effects are also compared with those reported in surveys of other types of reviews. \n \nFurther potentially relevant evidence is incorporated to address gaps identified in the literature. A detailed analysis is provided of the contribution of different sources of data for adverse drug reactions using 58 included studies from a case study systematic review. The same case study systematic review is then used to measure the performance of adverse effects search filters in MEDLINE and EMBASE. \n \nFinally 242 included papers from a series of 26 systematic reviews are evaluated to strengthen the evidence base regarding adverse effects search filters and to assess individual adverse effects search terms in MEDLINE, EMBASE, and Science Citation Index (SCI). \n \nThe strengths and weaknesses of the analyses are discussed and implications for practice and guidance presented along with recommendations for future research. \n
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 | 0.685 | 0.854 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.040 | 0.031 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.027 | 0.029 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".