Scoping review of potential quality indicators for hip fracture patient care
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to identify existing or potential quality of care indicators (ie, current indicators as well as process and outcome measures) in the acute or postacute period, or across the continuum of care for older adults with hip fracture. DESIGN: Scoping review. SETTING: All care settings. SEARCH STRATEGY: English peer-reviewed studies published from January 2000 to January 2016 were included. Literature search strategies were developed, and the search was peer-reviewed. Two reviewers independently piloted all forms, and all articles were screened in duplicate. RESULTS: The search yielded 2729 unique articles, of which 302 articles were included (11.1%). When indicators (eg, in-hospital mortality, acute care length of stay) and potential indicators (eg, comorbidities developed in hospital, walking ability) were grouped by the outcome or process construct they were trying to measure, the most common constructs were measures of mortality (outcome), length of stay (process) and time-sensitive measures (process). There was heterogeneity in definitions within constructs between studies. There was also a paucity of indicators and potential indicators in the postacute period. CONCLUSIONS: To improve quality of care for patients with hip fracture and create a more efficient healthcare system, mechanisms for the measurement of quality of care across the entire continuum, not just during the acute period, are required. Future research should focus on decreasing the heterogeneity in definitions of quality indicators and the development and implementation of quality indicators for the postacute period.
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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 | 0.069 | 0.314 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.047 | 0.040 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".