A12153 Quality of abstracts of pilot trials in heart failure
Bibliographic record
Abstract
Objectives: In this systematic survey, we analyzed the quality measured as the completeness of the reporting of pilot trial abstracts in heart failure based on the CONSORT extension for reporting abstracts of the pilot trial. We also identified factors that are associated with reporting quality Methods: We searched Medline (PUBMED), Cochrane Controlled Trials Register, Scopus and African-wide information databases for abstracts from heart failure pilot trials in humans published from 1 January 1990 to 30 November 2016. These were assessed to determine the extent of adherence to CONSORT extension checklist for reporting of abstracts of pilot trials. Identified studies were screened for inclusion based on title and abstract. Data were independently extracted by two reviewers in duplicate using the checklist Results: Two hundred and twenty-eight (228) articles were retrieved, out of which, 92 met the inclusion criteria. The mean CONSORT extension score was 8.3/16 (Standard Deviation 1.7), the least reported items were the source of funding (1% [1/92]), trial registration (13% [12/92]), randomization sequence (13% [12/92]), number randomized to each arm (16% [15/92]), and number analyzed in each arm (16% [15/92]). Multivariable regression analysis showed that pharmacological intervention pilot trials [Incidence rate ratio (IRR) = 0.83; 95% confidence interval (CI), 0.81–0.97], structured abstract (IRR = 1.10; 95% CI, 0.99–1.23), and CONSORT endorsement (IRR = 1.10; 955 CI, 1.09–1.23) were significantly associated with slightly better reporting quality. Conclusion: The quality of reporting of abstracts of heart failure pilot trials from was suboptimal and influenced by use of structured abstract, journal endorsement of CONSORT statement and type of intervention. These findings are consistent with previous researches.
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 | 0.321 | 0.651 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.034 | 0.033 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".