Reporting standards for guideline-based performance measures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The Guidelines International Network (G-I-N) aims to promote high quality clinical guideline development and implementation. Guideline-based performance measures are a key implementation tool and are widely used internationally for quality improvement, quality assurance, and pay for performance in health care. There is, however, no international consensus on best methods for guideline-based performance measures. In order to address this issue, the G-I-N Performance Measures Working Group aimed to develop a set of consensus-based reporting standards for guideline-based performance measure development and re-evaluation. METHODS: Methodology publications on guideline-based performance measures were identified from a systematic literature review and analyzed. Core criteria for the development and evaluation process of guideline-based performance measures were determined and refined into draft standards with an associated rationale and description of the evidence base. In a two-round Delphi-process, the group members appraised and approved the draft standards. After the first round, the group met to discuss comments and revised the drafts accordingly. RESULTS: Twenty-one methodology publications were reviewed. The group reached strong consensus on nine reporting standards concerning: (1) selection of clinical guidelines, (2) extraction of clinical guideline recommendations, (3) description of the measure development process, (4) measure appraisal, (5) measure specification, (6) description of the intended use of the measure, (7) measure testing/validating, (8) measure review/re-evaluation, and (9) composition of the measure development panel. CONCLUSIONS: These proposed international reporting standards address core components of guideline-based performance measure development and re-evaluation. They are intended to contribute to international reporting harmonization and improvement of methods for performance measures. Further research is required regarding validity, acceptability, and practicality.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it