A systematic review of quality indicators for evaluating pediatric trauma care
Bibliographic record
Abstract
OBJECTIVE: Trauma care provides injured children with life- and limb-saving treatment, but it is unclear if the proper tools have been developed to measure the quality of care delivered. We sought to systematically review the literature on quality indicators for evaluating pediatric trauma care. DATA SOURCES AND STUDY SELECTION: We searched MEDLINE (1950- January 14, 2009), EMBASE (1980-week 2, 2009), CINAHL (1982- week 2, 2009) and The Cochrane Library (4th Quarter 2008) from the earliest available date to January 14, 2009, plus the Gray Literature, select journals by hand, reference lists, and articles recommended by experts in the field. Studies were selected that used one or more quality indicators to evaluate the quality of care delivered to patients 18 yrs of age or younger with a major traumatic injury. DATA EXTRACTION AND DATA SYNTHESIS: The literature search identified 6869 citations. Review of abstracts led to the retrieval of 538 full-text articles for assessment; 12 articles were selected for review. Of these, five (42%) articles were case series and five (42%) articles were cohort studies. Two articles included control groups, a before-and-after case series, and a nonrandomized controlled trial. A total of 120 quality indicators in pediatric trauma care were identified, predominantly measures of prehospital and hospital processes and outcomes of care. We did not identify any prehospital structure or posthospital or secondary injury prevention quality indicators. Among multiple trauma patients, deficiencies in the quality of care ranged from 8% to 45% of patients, with 6% to 32% of deaths in hospital judged to be preventable on peer review. CONCLUSIONS: There is limited experimental research regarding quality indicators in pediatric trauma care, but the literature suggests that deficiencies exist in the quality of care. Future research is needed to develop and evaluate patient-centered pediatric-specific indicators that cover the full spectrum of trauma care.
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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 | 0.043 | 0.189 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.033 | 0.034 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".