Quality Evaluation of Clinical Practice Guidelines and the Recommendations for Esophageal Cancer
Bibliographic record
Abstract
Background: In the process of diagnosis and treatment of esophageal cancer, the clinical guidelines is of great significance for the medical behavior of clinicians and application of new research results. However, a non-standard formulation of the guidelines may mislead clinicians and bring unnecessary financial burden to patients. This study is to evaluate the quality of the guidelines. Methods: We identified the clinical practice guidelines or recommendations published in the English database since 2018 that guide for the diagnosis or treatment of esophageal cancer. Two reviewers used RIGHT checklist to evaluate the quality of eligible guidelines independently. Results: A total of 15 guidelines and recommendations meet the inclusion criteria. Six of them are from China, four from the United States, three from Japan, and the remaining two are from Canada and Germany. Among them, there are 4 in Chinese and 11 in English. The evaluation of these guidelines and recommendations showed that the reporting proportion of 4 articles was less than 50%. The average reporting proportion of 15 articles is 60.00%. Of the seven domains covered by RIGHT checklist, “basic information” has the highest reporting proportion (78.89%) and the lowest is “review and quality assurance” (13.33%). Of all sub-items, 3 are 100% reported, 13 (37.14%) sub-items are less than 50%. There is no difference between the Chinese guide and the English guide (P= 0.442). Conclusions: The reporting quality of the esophageal cancer guidelines published in the past three years is moderate, but there are some deficiencies in the domains of “review and quality assurance”, “funding and declaration and management of interests” and “other information”. Guideline developers should strictly follow the standard to improve the quality.
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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 | 0.387 | 0.708 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".