Developing a Protocol for the External Validation of a Clinical Prediction Model for the Diagnosis of Immune Thrombocytopenia
Bibliographic record
Abstract
Defined as a platelet count <100x109/L with no known cause, immune thrombocytopenia (ITP) is a diagnosis of exclusion, meaning other thrombocytopenic conditions must be ruled out before establishing the ITP diagnosis. This can lead to errors, unnecessary exposures to expensive and harmful treatments, and increased patient anxiety and distress. In the absence of a standardized diagnostic test, a clinical prediction model, called the Predict-ITP tool, was developed to aid hematologists in establishing the ITP diagnosis among patients who present with thrombocytopenia. Based on a cohort of 839 patients referred to an academic hematology clinic and using penalized logistic regression, the following predictor variables for the ITP diagnosis were identified: 1) high platelet variability index; 2) lowest platelet count; 3) highest mean platelet volume; and 4) history of a major bleed. Internal validation was completed using bootstrap resampling, and showed good discrimination and excellent calibration. Following internal validation and prior to implementation, the Predict-ITP Tool must undergo external validation by evaluating the tool’s performance in a different cohort. A study protocol was developed with the objective of externally validating the Predict-ITP Tool by collecting data from 960 patients from 11 clinics across Canada. The tool will compute the probability of ITP using information available at the time of the initial consultation, and results will be compared with either the local hematologist’s diagnosis at the end of follow-up or the adjudicated diagnosis. Discrimination (the ability to differentiate between patients with and without ITP) and calibration (the agreement between predicted and actual classifications) of the tool will be assessed. The Predict-ITP Tool must demonstrate good discrimination (c-statistic ≥ 0.8) and excellent calibration (calibration-in-the-large close to 0; calibration slope close to 1) to achieve external validation. If implemented, this tool will improve diagnostic accuracy and reduce delays in diagnosis and unnecessary treatments and investigations.
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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 | 0.168 | 0.176 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".