The reporting of studies using routinely collected health data was often insufficient
Bibliographic record
Abstract
OBJECTIVES: To assess reporting quality of studies using routinely collected health data (RCD) to inform the REporting of studies Conducted using Observational Routinely collected health Data (RECORD) guideline development. STUDY DESIGN AND SETTING: PubMed search for observational studies using RCD on any epidemiologic or clinical topic. Sample of studies published in 2012. Evaluation of five items based on the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guideline and eight newly developed items for RCD studies. RESULTS: Of 124 included studies, 39 (31.5%) clearly described its design in title or abstract. Complete information to frame a focused research question, that is, on the population, intervention/exposure, and outcome, was provided for 51 studies (41.1%). In 44 studies where definitions of codes or classification algorithms would be necessary to operationalize such a research question, only nine (20.5%) reported all items adequately. In 81 studies describing multivariable analyses, 54 (66.7%) reported all variables used for modeling and 34 (42.0%) reported basic details required for replication. Database linkage was reported adequately in 12 of 41 studies (29.3%). Statements about data sharing/availability were rare (5/124; 4%). CONCLUSION: Most RCD studies are insufficiently reported. Specific reporting guidelines and more awareness and education on their use are urgently needed.
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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 | 0.662 | 0.869 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".