Abstract reporting in randomized clinical trials of acute lung injury: An audit and assessment of a quality of reporting score*
Bibliographic record
Abstract
OBJECTIVES: To evaluate the quality of reporting among abstracts of randomized controlled trials (RCTs) in acute lung injury and to highlight important trial information for abstract inclusion. DESIGN: Audit of published RCT abstracts. SAMPLE: A total of 56 RCTs, identified in MEDLINE, EMBASE, HEALTHSTAR, CINAHL, and the Cochrane Central Register of Controlled Trials. MEASUREMENTS AND MAIN RESULTS: We used a research focus group and published literature on suggested content for abstracts of original articles to generate a list of 32 recommended items. The focus group reduced this list to a 20-item long form list of highly relevant criteria and a 12-item short form list of essential criteria for inclusion in abstracts of RCTs in acute lung injury. After pilot testing the abstract appraisal form, we evaluated abstracts independently and in duplicate. We scored the quality of reporting of each abstract by dividing the number of criteria fulfilled by the number applicable. Although abstracts described the study objectives and interventions well and the participants, outcomes, and conclusions to an intermediate extent, key deficiencies were noted in reporting the study methods, setting, and results. Mean quality of reporting scores were significantly higher for structured compared with unstructured abstracts using the 32-item, 20-item, and 12-item lists (p = .008, .014, and <.0001, respectively), especially for abstracts published after 1990 (p = .004, .017, and .001, respectively). The 20-item and 12-item lists correlated well with the 32-item list (r = .89 and .62, respectively) and with one another (r = .73). CONCLUSIONS: Key design features and results are frequently under-reported in RCT abstracts, particularly among unstructured abstracts. Checklists may aid authors and editors in prioritizing important criteria for inclusion in RCT abstracts.
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 | 0.672 | 0.790 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.035 | 0.034 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".