Development of rapid guidelines: 1. Systematic survey of current practices and methods
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: Guidelines in the healthcare field generally should contain evidence-based recommendations to inform healthcare decisions. Guidelines often require 2 years or more to develop, but certain circumstances necessitate the development of rapid guidelines (RGs) in a short period of time. Upholding methodological rigor while meeting the reduced development timeframe presents a challenge for developing RGs. Our objective was to review current practices and standards for the development of RGs. This is the first of a series of three articles addressing methodological issues around RGs. METHODS: We conducted a systematic survey of methods manuals and published RGs to identify reasons for the development of RGs. Data sources included existing guideline manuals, published RGs, Trip Medical Database, MEDLINE, EMBASE and communication with guideline developers until February 2018. RESULTS: We identified 46 guidelines that used a shortened timeframe for their development. Nomenclature describing RGs varied across organisations, wherein the United States Centers for Disease Control and Prevention produced 'Interim Guidelines', the National Institute for Health and Care Excellence in the United Kingdom developed 'Short Clinical Guidelines', and WHO provided 'Rapid Advice'. The rationale for RGs included response to emergencies, rapid increases in cases of a condition or disease severity, or new evidence regarding treatment. In general, the methods to assess the quality of evidence, the consensus process and the management of the conflict of interest were not always clear. While we identified another 11 RGs from other institutions, there was no reference to timeframe and reasons for conducting a RG. The three organisations mentioned above provide guidance for the development of RGs. CONCLUSIONS: There is a lack of standardised nomenclature and definitions regarding RGs and there is inconsistency in the methods described in manuals and in RG. It is therefore important that all RGs provide a detailed and transparent description of their methods in order for readers and end-users to be able to assess their quality and validate their findings.
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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 | 0.091 | 0.174 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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