Level of evidence in high impact surgical literature: the way forward
Bibliographic record
Abstract
Evidence-based medicine stipulates that clinical decision-making should revolve around scientific evidence. The goal of the present study is to evaluate the methodological quality of surgical research recently published in JAMA Surgery, International Journal of Surgery, and British Journal of Surgery, the three surgical journals with the highest impact factor. An electronic search of the PUBMED database was performed to retrieve all articles published in the JAMA Surgery, International Journal of Surgery, and British Journal of Surgery in the year 2022. Three authors independently reviewed all retrieved articles and methodological designs of the publications were analyzed and rated using a modification of the Oxford Centre for Evidence-Based Medicine Levels of Evidence (Oxford Levels of Evidence scale). The initial search identified 1236 articles of which 809 were excluded after title and abstract screening. The remaining 427 underwent full text/methods read, of which 164 did not meet the inclusion/exclusion criteria. A total of 273 studies were included in the analysis. The average level of evidence was 2.5 ± 0.8 across all studies assessed. The majority of study designs were comprised of retrospective cohorts (n = 119), prospective cohorts (n = 47), systematic reviews of non RCTs (n = 39), and RCTs (n = 37). There was no significant difference in the average level of evidence between the top three journals (p = 0.50). Most clinical studies in the highest impact factor surgical journals are of level III evidence, consistent with earlier literature. However, our analysis demonstrates a relatively higher percentage of LOE I and II compared to what was previously published in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.139 | 0.417 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".