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Record W2009708623 · doi:10.1001/archsurg.2009.289

Quality Indicators for Evaluating Trauma Care

2010· article· en· W2009708623 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueArchives of Surgery · 2010
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversity of CalgaryFoothills Medical Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineCINAHLMEDLINEData extractionSystematic reviewGrey literatureEmergency medicineMedical emergencyPsychological interventionIntensive care medicineNursing

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.105
GPT teacher head0.400
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it