Quality Indicators in Pediatric Orthopaedic Surgery: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The ability to measure health system quality has become a priority for governments, the private sector, and the public. Quality indicators (QIs) refer to clear, measurable items related to outcomes. The use of QIs can initiate local quality improvement and track changes in quality over time as interventions are implemented. QUESTIONS/PURPOSES: We identified existing evidence-based indicators of quality pediatric orthopaedic care and evaluated published QIs that may be applicable to pediatric orthopaedic care. SEARCH STRATEGY: Using five standard search engines we searched the literature using terms such as "quality indicators," "orthopaedic surgery," and "pediatric." Study selection was performed in a stepwise manner, first by title, then abstract, and then full-text review. Of the 604 citations identified, 13 articles were selected for inclusion. Eight papers included only pediatric patients. RESULTS: The most commonly reported indicator was mortality followed by postoperative complications. Reoperation and readmission rates were also reported along with patient-centered QIs, although with less frequency. CONCLUSION: Although mortality and postoperative complications were the most frequently reported QIs, concern for their applicability was raised because of their relative infrequency in pediatrics. Patient-centered QIs appear to be the most useful tools reported, although their use is somewhat limited in the published literature. Although there are benefits and drawbacks to all reported QIs, patient-centered and surgeon-defined outcomes along with cost-effectiveness have important roles in evaluating the quality of pediatric orthopaedic 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 | 0.019 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.020 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 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".