Surgical Outcomes Through the Patient's Eyes: A Scoping Review of Patient-Reported Outcome Measures in Surgery
Bibliographic record
Abstract
INTRODUCTION: Patient-reported outcome measures (PROMs) have gained significant recognition for their crucial role in evaluating patient-centered outcomes in surgical interventions. This scoping review aims to assess the use of PROMs across various specialties and identify trends in PROM implementation. METHODS: A literature search was conducted using PubMed databases, including studies published between January 2010 and August 2024. Data on the surgical field, the number and types of PROMs used, and the outcome domains explored were extracted. The findings were synthesized using descriptive analysis. RESULTS: From an initial pool of 1089 articles, this review included 275 papers, comprising 72 clinical trials and 203 observational studies. Orthopedic surgery emerged as the most frequent field utilizing PROMs, accounting for 100 (36.4%) studies. Meanwhile, other less commonly represented specialties such as general surgery accounted for 6.6% (n = 18). While vascular surgery, urology, and ear, nose, and throat were each underrepresented at 2.2% (n = 6). Notably, 112 (40.7%) studies focused on a single PROM, with the Visual Analog Scale being the most commonly utilized tool (n = 36, 13.0%). This was followed by the Knee Injury and Osteoarthritis Outcome Score (n = 31, 11.2%), Patient-Reported Outcomes Measurement Information System (n = 24, 8.7%), and EuroQol-5 Dimension (n = 24, 8.7%). CONCLUSIONS: The application of PROMs in surgical research is expanding, particularly in orthopedic surgery, while specialties such as vascular, urology, and ear, nose, and throat remain underrepresented. While the majority of studies continue to focus on single outcomes, there is a growing interest in multidimensional evaluations, suggesting a future direction for more comprehensive patient-centered assessments.
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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 | 0.020 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".