Reporting quality of abstracts of trials published in top five pain journals: a protocol for a systematic survey
Bibliographic record
Abstract
INTRODUCTION: Abstracts of randomised controlled trials (RCTs) are often the first and the only source read in a journal by busy healthcare providers. This necessitates good reporting of abstracts. The quality of reporting of abstracts, though gradually improving over time, is still not uniform across medical journals. Improvement in completeness of reporting of abstracts has been documented in general medical journals after the publication of the consolidated standards of reporting trials (CONSORT) extension for abstracts in 2008. Currently, this aspect has not been assessed with regards to pain journals. This study aims to compare the completeness of reporting of abstracts before and after the publication of CONSORT statement for abstracts in five pain journals. METHODS AND ANALYSES: The abstracts of RCTs published from 1 January 2005 to 31 December 2007 (pre-CONSORT) and from 1 January 2013 to 31 December 2015 (post-CONSORT) will be assessed for the quality of reporting. Studies without abstracts, non-English abstracts, abstracts not reporting on RCTs or on humans and conference abstracts will be excluded. A thorough search of MEDLINE will be carried out in April 2016. All identified studies will be screened for inclusion based on titles and abstracts. Data will be extracted by two sets of independent reviewers for each abstract in duplicate regarding compliance with CONSORT statement for abstracts. Full-text review will be performed to obtain additional characteristics which are likely to affect reporting quality. The unadjusted and adjusted differences in the mean number of items reported will be analysed using a two sample t-test and generalised estimation equation in SPSS. ETHICS AND DISSEMINATION: As far as we know, this is the first study to evaluate reporting quality of abstracts of pain journals based on CONSORT extension for abstracts. The findings of this study will be disseminated by a presentation at a conference and through publication in a peer-reviewed journal. Ethics committee approval was not sought for this survey.
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 | 0.363 | 0.394 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.017 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.035 | 0.014 |
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".