Bibliographic record
Abstract
OBJECTIVES: To systematically review the literature on quality indicators (QIs) for evaluating trauma care, identify QIs, map their definitions, and examine the evidence base in support of the QIs. DATA SOURCES: We searched MEDLINE, EMBASE, CINAHL, Cochrane Database of Systematic Reviews, Cochrane Database of Abstracts of Reviews of Effects, and Cochrane Central Register of Controlled Trials from the earliest available date through January 14, 2009. To increase the sensitivity of the search, we also searched the grey literature and select journals by hand, reviewed reference lists to identify additional studies, and contacted experts in the field. STUDY SELECTION AND DATA EXTRACTION: We selected all articles that identified or proposed 1 or more QIs to evaluate the quality of care delivered to patients with major traumatic injuries. Minimum inclusion criteria were a description of 1 or more QIs designed to evaluate patients with major traumatic injuries (defined as multisystem injuries resulting in hospitalization or death) and focused on prehospital care, hospital care, posthospital care, or secondary injury prevention. DATA SYNTHESIS: The literature search identified 6869 citations. Review of abstracts led to the retrieval of 538 full-text articles for assessment, of which 192 articles were selected for review. Of these, 128 (66.7%) articles were original research, predominantly trauma database case series (57 [29.7%]) and cohort studies (55 [28.6%]), whereas 37 (19.3%) were narrative reviews and 8 (4.2%) were guidelines. A total of 1572 QIs in trauma care were identified and classified into 8 categories: non-American College of Surgeons Committee on Trauma (ACS-COT) audit filters (42.0%), ACS-COT audit filters (19.1%), patient safety indicators (13.2%), trauma center/system criteria (10.2%), indicators measuring or benchmarking outcomes of care (7.4%), peer review (5.5%), general audit measures (1.8%), and guideline availability or adherence (0.8%). Measures of prehospital and hospital processes (60.4%) and outcomes (22.8%) were the most common QIs identified. Posthospital and secondary injury prevention QIs accounted for less than 5% of QIs. CONCLUSIONS: Many QIs for evaluating the quality of trauma care have been proposed, but the evidence to support these indicators is not strong. Practical recommendations to select QIs to measure the quality of trauma care will require systematic reviews of identified candidate indicators and empirical studies to fill the knowledge gaps for postacute QIs.
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 | 0.108 | 0.324 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.032 | 0.034 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".