Quality of Reporting on Guideline, Protocol, or Algorithm Implementation in Adult Trauma Centers
Bibliographic record
Abstract
OBJECTIVE: To appraise the quality of reporting on guideline, protocol, and algorithm implementations in adult trauma settings according to the Revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0). BACKGROUND: At present we do not know if published reports of guideline implementations in trauma settings are of sufficient quality to facilitate replication by other centers wishing to implement the same or similar guidelines. METHODS: A systematic review of the literature was conducted. Articles were identified through electronic databases and hand searching relevant trauma journals. Studies meeting inclusion criteria focused on a guideline, protocol, or algorithm that targeted adult trauma patients ≥18 years and/or trauma patient care providers, and evaluated the effectiveness of guideline, protocol, or algorithm implementation in terms of change in clinical practice or patient outcomes. Each included study was assessed in duplicate for adherence to the 18-item SQUIRE 2.0 criteria. The primary endpoint was the proportion of studies meeting at least 80% (score ≥15) of SQUIRE 2.0. RESULTS: Of 7368 screened studies, 74 met inclusion criteria. Thirty-nine percent of studies scored ≥80% on SQUIRE 2.0. Criteria that were met most frequently were abstract (93%), problem description (93%), and specific aims (89%). The lowest scores appeared in the funding (28%), context (47%), and results (54%) criteria. No study indicated using SQUIRE 2.0 as a guideline to writing the report. CONCLUSIONS: Significant opportunity exists to improve the utility of guideline implementation reports in adult trauma settings, particularly in the domains of study context and the implications of context for study outcomes.
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 | 0.719 | 0.882 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".