Quality of abstracts of randomized control trials in five top pain journals: A systematic survey
Bibliographic record
Abstract
BACKGROUND: The reporting quality of abstracts of randomized control trials (RCTs) is inadequate despite the publication of consolidated standards of reporting trials extension for abstracts (CONSORT-A). We compared the reporting quality of abstracts in pain journals before and after the publication of CONSORT-A. METHODS: We searched MEDLINE in April-2016 for RCTs published in five pain journals: Pain, Pain Physician, European Journal of Pain, Clinical Journal of Pain and Pain Practice for pre- and post-CONSORT-A period (2005-2007 and 2013-2015). Data were extracted in duplicate from 250 abstracts for compliance with CONSORT-A, and for items known to affect reporting quality: journal endorsement of CONSORT, number of trial centers, sample-size, type of intervention, industry-sponsorship and significance of results. The primary outcome was mean number of items reported and the secondary outcome was the reporting of each item. We used logistic regression and Poisson regression for analyses. RESULTS: Most trials were single centric (76%), had sample size <100 (63%), involved pharmacological intervention (59%) and were non-industry funded (70%). The mean number of items reported was better for 2013-2015 (mean difference 0.94; 95% confidence-interval [CI]: 0.50-1.38, p < 0.001). Post-CONSORT-A, trials were more likely to report as randomized in the title (odds ratio (OR) 2.69; 95% CI 1.61-4.49), describe eligibility criteria and settings (OR 2.47; 95% CI 1.35-4.54), provide effect size and precision for primary outcome (OR 2.47; 95% CI 1.19-5.16), inform harms (OR 1.80; 95% CI 1.05-3.07) and report trial registration (OR 5.13; 95% CI 1.44-18.32). Post-CONSORT-A period (incident rate ratio (IRR) 1.15; 95% CI 1.07-1.24), endorsement of CONSORT statement by the journal (IRR 1.08; 95% CI 1.02-1.14), multi-centric studies (IRR 1.14; 95% CI 1.08-1.20), and studies with pharmacological interventions (IRR 1.07; 95% CI 1.02-1.13) were significantly associated with reporting of more items. CONCLUSIONS: Abstract reporting for trials in pain literature was better in the post-CONSORT-A period, but there is room for improvement.
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 | 0.301 | 0.626 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.035 | 0.037 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".