The platelet variability index tool to determine the likelihood of immune thrombocytopenia (ITP): User testing and tool refinement
Bibliographic record
Abstract
Abstract Introduction The diagnosis of immune thrombocytopenia (ITP) is challenging due to the lack of a reliable diagnostic laboratory test. The platelet variability index (PVI) is a simple metric that captures the magnitude of the platelet count fluctuations over time and the severity of the thrombocytopenia from 3 or more consecutive platelet count measurements. An elevated PVI score, reflecting a high degree of platelet variability and severe thrombocytopenia, can distinguish between patients with ITP and patients with thrombocytopenia from non-immune causes (Li et al, Blood Advances 2021). We conducted a usability study to refine the PVI application based on user feedback. Methods We recruited 5 hematologist users from Canada, the U.S., and Italy to provide feedback on the presentation and utility of the web-based application of the PVI tool. After a virtual presentation of the application to users, we solicited unstructured feedback in person and by email followed by a group discussion. We then delivered a structured electronic survey to users to evaluate 5 reporting options for the PVI score, which varied in the wording of the interpretation summary and in the number of likelihood categories (high, moderate, low +/- very low likelihood of ITP). Options for the interpretation summary included a combination of the expected number of ITP patients (out of 100) in each category based on the previously published derivation cohort (Li et al, Blood Advances 2021) and the overall likelihood category. Respondents rated each option for clarity, clinical utility, interpretability, and workflow using a Likert scale with an opportunity for free text feedback. The final PVI application format was presented back to users for final comment and approval. Results During the initial unstructured feedback and discussion, users identified the need for a hierarchical designation of ITP likelihood based on the PVI score rather than a simple dichotomous result (ITP is likely or unlikely). All 5 options presented in the survey were rated as easy to understand, clinically useful, helpful for interpreting the likelihood of ITP, and applicable to clinical workflow by most respondents (80%, n=4). All respondents favored a concise summary of the patient's likelihood of ITP rather than the expected prevalence numbers. Most respondents (60%, n=3) preferred the inclusion of a “very low likelihood of ITP” category to improve interpretability, align with prevalence estimates, and enhance confidence in ruling out the ITP diagnosis for patients with the lowest scores. The final PVI application format that was agreed upon by all users included a likelihood category (from very low likelihood of ITP to high likelihood of ITP) and the expected prevalence numbers for each category presented as a table for reference. For example: “PVI Score = 2. This patient has a very low likelihood of ITP. For PVI score 0 – 2, less than 3 patients per 100 would be expected to have ITP.” Conclusions User feedback on the PVI tool focused on clear and concise interpretation summaries for the likelihood of ITP based on the PVI score. Further studies will assess the impact of the PVI tool on clinical outcomes for patients with thrombocytopenia who are suspected of having ITP.
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 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 | 0.069 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".