Completeness of reporting of quality improvement studies in neonatology is inadequate: a systematic literature survey
Bibliographic record
Abstract
INTRODUCTION: Quality improvement (QI) is a growing field of inquiry in healthcare, but the reporting quality of QI studies in neonatology remains unclear. We conducted a systematic survey of the literature to assess the reporting quality of QI studies and factors associated with reporting quality. METHODS: We searched Medline for publications of QI studies from 2016 to 16 April 2020. Pairs of reviewers independently screened citations and assessed reporting quality using a 31-item modified Standards for Quality Improvement Reporting Excellence, 2nd edition (SQUIRE 2.0) checklist. We reported the number (percentage) of studies that reported each item and their corresponding 95% CIs. We used Poisson regression to explore factors associated with reporting quality, namely, journal endorsement of SQUIRE 2.0, declaration of funding sources, year of publication and number of authors. The results were reported as incidence rate ratio (IRR) and 95% CI. RESULTS: Of 1921 citations, 336 were eligible; among them, we randomly selected 100 articles to assess reporting quality. The mean (standard deviation) number of SQUIRE 2.0 items adhered to was 22.0 (4.5). Percentage of articles reporting each item varied from 26% to 100%. Journal endorsement of SQUIRE 2.0 (IRR=1.11, 95% CI 1.02 to 1.21, p=0.015), declaration of funding sources and increasing number of authors were significantly associated with better reporting. CONCLUSIONS: Reporting quality of QI studies in neonatology is inadequate. Endorsing the SQUIRE 2.0 guideline is a step that journals can implement to enhance the completeness of reporting.
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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 | 0.298 | 0.618 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.023 | 0.030 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".