Systematic Review of the Quality of Economic Evaluations in the Otolaryngology Literature
Bibliographic record
Abstract
OBJECTIVE: To evaluate the quality of economic evaluations published in the otolaryngology--head and neck surgery literature, which will identify methodologic weaknesses that can be improved on in future studies. A secondary objective is to identify factors that may be associated with higher quality economic evaluations. DATA SOURCES: Ovid Medline (including PubMed), Embase, and the National Health Services Economic Evaluation databases. REVIEW METHODS: A systematic search was performed of the aforementioned databases according to PRISMA guidelines. The search was performed using otolaryngology key terms combined with the term cost. A manual search of 36 otolaryngology journals was also performed. Included studies were graded using the Quality of Health Economics Studies instrument, a 16-item checklist providing a total quality score of 100. RESULTS: Fifty studies were identified, and the mean quality rating was 54.7/100 (SD = 30.9). The most commonly omitted methodology components were a lack of discussion of limitations and biases, failure to address the negative outcomes of examined interventions, and a lack of a robust sensitivity analysis. Higher quality economic evaluations were associated with a higher journal impact factor (correlation coefficient r = 0.62, P = .0001), having an author with a PhD in health economics (r = 0.56, P = .0001), and having authors who have published prior economic evaluations (r = 0.46, P = .001). CONCLUSION: Results from this study have demonstrated that there are several methodological domains that can be improved on when publishing economic evaluations in the otolaryngology literature. Authors should follow recommended methodological and reporting guidelines to optimize the transparency and accuracy of the overall conclusions.
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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 | 0.182 | 0.563 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".