The Inclusion and Intended Use of Prediction Models in Clinical Guidelines: A systematic review of five clinical domains in four countries
Bibliographic record
Abstract
OBJECTIVES: Prediction models can be used to support medical decision-making, but their integration into clinical guidelines remains unclear. Despite the abundance of published models, it is unknown how many are included in clinical guidelines and whether their recommended use in the guideline aligns with model's intended use at development. We investigated the inclusion of prediction models in clinical guidelines and examined any discrepancies between the intended use described in the model development or validation studies and their recommendation in the clinical guidelines in which they are mentioned. STUDY DESIGN AND SETTING: We systematically reviewed clinical guidelines across five clinical domains (pulmonary embolism, preeclampsia, sepsis, dementia, and lung cancer) in four countries (the United States, the United Kingdom, Canada, and the Netherlands). For each prediction model included in the guideline, we identified the corresponding model development and validation studies. Data on clinical setting and intended use of the prediction model were extracted from the guidelines, the development and external validation papers. RESULTS: A total of 20 clinical guidelines were included, identifying nine unique prediction models that were externally validated in 59 studies. Pulmonary embolism had the highest frequency of prediction models in guidelines (4/4), followed by sepsis (3/4), lung cancer and preeclampsia (both 1/4), and dementia (0/4). We identified large discrepancies between the setting and intended use of the models at development or in validation studies, and the guidance provided in guidelines, particularly on aspects such as the clinical context/setting, the timing of use, and the specific patient population. CONCLUSION: Clinical guidelines occasionally recommended the use of prediction models. Many guidelines provided limited instructions on how to operationalize these models and often recommended using the model in a specific clinical scenario without addressing any further key differences from development or validation. When included, typically there were inconsistencies between the intended use of the model and guideline recommendation. There is need for greater standardization in how prediction models are incorporated into guidelines, and to ensure that their recommended use aligns with their intended purpose and validation evidence.
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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 | 0.084 | 0.326 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| 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".