The Coags Uncomplicated App: Fulfilling Educational Gaps Around Diagnosis and Laboratory Testing of Coagulation Disorders
Bibliographic record
Abstract
BACKGROUND: Patients with coagulation disorders may present to a variety of physician specialties; however, accurate and efficient diagnosis can be challenging for physicians not specialized in hematology, due to identified gaps in knowledge around appropriate laboratory assays and interpretation of test results. Coags Uncomplicated was developed to fill this unmet educational need by increasing practical knowledge of coagulation disorders among nonexpert physicians and other health care professionals (HCPs) in a point-of-care (POC) setting. OBJECTIVE: The aim of this study was to assess patterns of use of the mobile app Coags Uncomplicated, a tool designed to support education regarding accurate and efficient diagnosis of bleeding disorders. METHODS: App metrics were obtained by tracking registered user data. Additionally, a survey was distributed to registered users, to assess circumstances and frequency of use. RESULTS: The most common specialties of 7596 registered US users were hematology-oncology (n=1534, 20.19%), hematology (n=1014, 13.35%), and emergency medicine (n=1222, 16.09%); most identified as physicians (n=4082, 53.74%). Specialties accounting for the greatest numbers of screen views were hematology-oncology (99,390 views), hematology (47,808 views), emergency medicine (23,121 views), and internal medicine (22,586 views). The most common diagnostic endpoints reached were disseminated intravascular coagulation (DIC; 2713 times), liver disease effect (2108 times), and vitamin K deficiency (1584 times). Of 3424 users asked to take the survey, 262 responded (7.65%); most were physicians in direct clinical care (71%) and specialized in hematology-oncology (39%) or emergency medicine (21%). Most frequent use was reported by hematologists (69%, ≥6 times) and hematologists-oncologists (38%, ≥6 times). Most physicians (89.2%) reported using the app for patient-case-related education around appropriate use of laboratory tests in diagnostic evaluation. Physicians rated Lab Value Analyzer (mean 4.43) and Lab Test Algorithm (mean 4.46) tools highly on a 5-point "how helpful" scale and were likely to recommend the app to colleagues. CONCLUSIONS: App use among physicians and other HCPs is consistent with value as a POC educational tool, which may facilitate differential diagnoses and appropriate early consultation with hematologists.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".