The Necessity and Feasibility Assessment Tool of the Clinical Prediction Model for Individual Prognosis Before Its Startup: A Multi‐Sectoral Delphi Consensus Study
Bibliographic record
Abstract
OBJECTIVE: The overwhelming majority of prediction models have not been applied. An evidence-based review is needed to show that the new research is justified. This study aimed to develop an assessment tool for researchers and peer reviewers to conduct a rapid and comprehensive evaluation on the necessity and feasibility of planning clinical prediction model before its startup. METHODS: The framework for developing quality assessment tools was followed to develop the necessity and Feasibility Assessment Tool of CLInical Prediction models for individual prognosis (FATCLIP). Firstly, the scope, framework, and item pool of the FATCLIP was identified by a steering group comprising 15 experts through a web-based meeting. Then, an iterative Delphi process was conducted to refine the FATCLIP, in which the Delphi group enrolled 34 experts from multidiscipline, including epidemiologists, statisticians, clinicians, evidence-based medicine specialists, health care administrators and academic journal editors. RESULTS: Through twice steering group meetings and 2 rounds of the Delphi process, the framework of FATCLIP was determined based on expert consensus, including 6 domains and 31 signaling questions. The six domains were as follows: prediction outcome, review of existing models, candidate predictors, data, development and validation, and application and extension. At the same time, the usage manual of FATCLIP was also presented. CONCLUSIONS: The FATCILP aims to assist researchers and peer reviewers to detect potential challenges during the development and application of the clinical prediction model for individual prognosis before its start-up, so that the research of clinical prediction models could be efficient and avoid research waste.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".