Quality-adjusted life years and surgical waiting list: Systematic review of the literature
Bibliographic record
Abstract
BACKGROUND The quality-adjusted life year (QALY) is a metric that is increasingly used today in the field of health economics to evaluate the value of different medical treatments and procedures. Surgical waiting lists (SWLs) represent a pressing problem in public healthcare. The QALY measure has rarely been used in the context of surgery. It would be interesting to know how many QALYs are lost by patients on SWLs. AIM To investigate the relationship between QALYs and SWLs in a systematic review of the scientific literature. METHODS The study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement. An unlimited search was carried out in PubMed, updated on January 19, 2024. Data on the following variables were investigated and analyzed: Specialty, country of study, procedure under study, scale used to measure QALYs, the use of a theoretical or real-life model, objectives of the study and items measured, the economic value assigned to the QALY in the country in question, and the results and conclusions published. RESULTS Forty-eight articles were selected for the study. No data were found regarding QALYs lost on SWLs. The specialties in which QALYs were studied the most in relation to the waiting list were urology and general surgery, with 15 articles each. The country in which the most studies of QALYs were carried out was the United States (n = 21), followed by the United Kingdom (n = 9) and Canada (n = 7). The most studied procedure was organ transplantation (n = 39), including 15 kidney, 14 liver, 5 heart, 4 lung, and 1 intestinal. Arthroplasty (n = 4), cataract surgery (n = 2), bariatric surgery (n = 1), mosaicplasty (n = 1), and septoplasty (n = 1) completed the surgical interventions included. Thirty-nine of the models used were theoretical (the most frequently applied being the Markov model, n = 34), and nine were real-life. The survey used to measure quality of life in 11 articles was the European Quality of Life-5 dimensions, but in 32 articles the survey was not specified. The willingness-to-pay per QALY gained ranged from $100000 in the United States to €20000 in Spain. CONCLUSION The relationship between QALYs and SWLs has only rarely been studied in the literature. The rate of QALYs lost on SWLs has not been determined. Future research is warranted to address this issue.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".