Applicability of the AGREE II Instrument in Evaluating the Development Process and Quality of Current National Academy of Clinical Biochemistry Guidelines
Bibliographic record
Abstract
BACKGROUND: Laboratory medicine practice guidelines (LMPGs) are an important part of clinical laboratory medicine. The Appraisal of Guidelines for Research and Evaluation II (AGREE II) instrument has been developed to evaluate the process of practice-guideline development and the quality of reporting. We assessed the applicability of AGREE II in assessing the National Academy of Clinical Biochemistry (NACB) LMPGs. METHODS: The NACB website was searched for all available LMPGs up to December 2011. Two independent appraisers used the AGREE II instrument to assess each LMPG identified by the search. Quality was assessed across 6 domains (scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence), comprising a total of 23 items and 2 overall assessments, each scored on a 7-point scale (1, strongly disagree, to 7, strongly agree). All scores were expressed as AGREE II calculated percentages (100% indicates that all items scored 7 by all appraisers). RESULTS: Eleven LMPGs were identified. All of the LMPGs provided some information seen as applicable to clinical practice by the appraisers. Only 5 of the LMPGs had overall scores ≥50%, with a median score of 42% (range: 8%-92%). Individual domain scores varied considerably from 0% to 100%. One guideline achieved a very high score on the instrument. CONCLUSIONS: The AGREE II instrument is applicable and useful to evaluate LMPGs. All domains were evaluated as being useful to assess LMPGs, some were addressed well (e.g., clarity of presentation), whereas others could be improved (e.g., applicability).
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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 | 0.337 | 0.490 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".