Use of Patient-reported Outcome Measures in Pediatric Orthopaedic Literature
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcomes are increasingly used as primary measures of success and impact of health care services. There is no current consensus on the efficacy of functional health status (FHS) or health-related quality of life (HRQoL) evaluation in pediatric orthopaedic surgery. The purpose of this study was to quantify and characterize the use of such patient-reported outcomes in the pediatric orthopaedic literature over the last several years. METHODS: A cross-sectional review of 6 orthopaedic journals-Journal of Bone and Joint Surgery, Bone and Joint Journal, Spine, Journal of Pediatric Orthopaedics A, Journal of Pediatric Orthopaedics B, and Journal of Children's Orthopaedics, was conducted by 2 independent reviewers. Articles were analyzed for patient-reported outcome usage over the time periods of 2000 to 2001, 2007 to 2008, and 2012 to 2013. RESULTS: A total of 189 articles (2.7%) were identified that used patient-reported outcomes in pediatric populations. A total of 71 (37.5%) of these articles used FHS tools, 95 (50.3%) used HRQoL tools and 23 (12.2%) used a combination of both. The use of FHS and HRQoL tools has increased over time, with 8 FHS and 2 HRQoL publications in the year 2000 expanding to 19 and 28, respectively, in 2013. Journal of Bone and Joint Surgery American Volume published both the highest total number of articles containing patient-reported outcome measures (57/189) and the highest number of articles specifically using FHS measures (28/71). Spine published the highest number of articles using HRQoL measures (43/95). The most frequently used FHS and HRQoL tools were the Lysholm knee score and the Scoliosis Research Society tools, respectively. Only 33/73 identified patient-based outcome instruments had been validated in a pediatric population. CONCLUSIONS: Patient-reported outcome measures are underutilized in the pediatric orthopaedic literature. Instruments are frequently used that are neither designed nor validated for a pediatric population. Consequently, further work is necessary to develop, validate, and implement pediatric-specific FHS and HRQoL tools to fully understand the impact of a clinical intervention on all aspects of patient quality of life.
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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 | 0.051 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.042 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".