Guidelines for squamous cell carcinoma of the head and neck: A systematic assessment of quality.
Bibliographic record
Abstract
We conducted a study to evaluate the quality of guidelines for squamous cell carcinoma of the head and neck (SCCHN) with the exception of nasopharyngeal cancer. Electronic searches were conducted of the U.S. National Guideline Clearinghouse, the Canadian Medical Association Infobase, the Guidelines International Network, the Scottish Intercollegiate Guidelines Network, the China Biology Medicine disc, PubMed, and Embase. Two independent reviewers assessed the eligible guidelines using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) instrument. The degree of agreement among these sources was evaluated by using the intraclass correlation coefficient (ICC). A total of 514 articles were found to be clinical-guideline-related, and 49 guidelines were included in our analysis. Scores were assigned for each of the AGREE II domains: scope and purpose (mean: 71.63% ± 2.80; median: 75%; ICC: 0.76), stakeholder involvement (mean: 43.37% ± 2.96; median: 50%; ICC: 0.93), rigor of development (mean: 45.63% ± 3.84; median: 42%; ICC: 0.83), clarity of presentation (mean: 68.08% ± 2.53; median: 72%; ICC: 0.85), applicability (mean: 32.41% ± 3.03; median: 29%; ICC: 0.92), and editorial independence (mean: 42.55% ± 4.57; median: 42%; ICC: 0.95). We considered a domain score of greater than 60% to represent an acceptable level of quality. We conclude that, overall, the quality of SCCHN guidelines is moderate in relation to international averages. Greater efforts are needed to provide high-quality guidelines that serve as a useful and reliable tool for clinical decision making in this field.
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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 | 0.170 | 0.516 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".