Strategies to assess the validity of recommendations: a study protocol
Bibliographic record
Abstract
BACKGROUND: Clinical practice guidelines (CPGs) become quickly outdated and require a periodic reassessment of evidence research to maintain their validity. However, there is little research about this topic. Our project will provide evidence for some of the most pressing questions in this field: 1) what is the average time for recommendations to become out of date?; 2) what is the comparative performance of two restricted search strategies to evaluate the need to update recommendations?; and 3) what is the feasibility of a more regular monitoring and updating strategy compared to usual practice?. In this protocol we will focus on questions one and two. METHODS: The CPG Development Programme of the Spanish Ministry of Health developed 14 CPGs between 2008 and 2009. We will stratify guidelines by topic and by publication year, and include one CPG by strata.We will develop a strategy to assess the validity of CPG recommendations, which includes a baseline survey of clinical experts, an update of the original exhaustive literature searches, the identification of key references (reference that trigger a potential recommendation update), and the assessment of the potential changes in each recommendation.We will run two alternative search strategies to efficiently identify important new evidence: 1) PLUS search based in McMaster Premium LiteratUre Service (PLUS) database; and 2) a Restrictive Search (ReSe) based on the least number of MeSH terms and free text words needed to locate all the references of each original recommendation.We will perform a survival analysis of recommendations using the Kaplan-Meier method and we will use the log-rank test to analyse differences between survival curves according to the topic, the purpose, the strength of recommendations and the turnover. We will retrieve key references from the exhaustive search and evaluate their presence in the PLUS and ReSe search results. DISCUSSION: Our project, using a highly structured and transparent methodology, will provide guidance of when recommendations are likely to be at risk of being out of date. We will also assess two novel restrictive search strategies which could reduce the workload without compromising rigour when CPGs developers check for the need of updating.
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.
How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".