What do letters to the editor publish about randomized controlled trials? A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: To identify published letters to the editor (LTE) written in response to randomized controlled trials (RCTs), determine the topics addressed in the letters, and to examine if these topics were affected by the characteristics and results of the RCTs. METHODS: Comparative cross-sectional study of a representative sample of RCTs from a set of high-impact medical journals (BMJ, Lancet, NEJM, JAMA, and Annals of Internal Medicine). RCTs and their published LTE were searched from these 5 journals in 2007. Data were collected on RCTs and their characteristics (author affiliation, funding source, intervention, and effect on the primary outcome) and the topics addressed in published LTE related to these RCTs. Analysis included chi-square and regression analysis (RCT characteristics) and thematic analysis (LTE topics). RESULTS: Of 334 identified RCTs, 175 trials had at least one LTE. Of these, 381 published LTE were identified. Most RCTs, tested drug interventions (68%), were funded by government (54%) or industry (33%), and described an intervention that had a positive impact on the primary outcome (62%). RCT authors were primarily affiliated with an academic centre (78%). Ninety percent of the 623 LTE topics concerned methodological issues regarding the analysis, intervention, and population in the RCT. There was a significant association between funding source and impact on outcomes (p = 0.002) or type of intervention tested (p = 0.001) in these trials. Clinical and "Other" LTE topics were more likely to be published in response to a government funded RCT (p = 0.005 and p = 0.033, respectively); no other comparisons were significant. CONCLUSIONS: This study showed that most LTE are about methodological topics, but found little evidence to support that these topics are affected by the characteristics or results of the RCTs. The lack of association may be explained by editorial censorship as a small proportion of LTE that are submitted are actually published.
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 | 0.236 | 0.776 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".