Web-Integration PROAFTN Methodology for Acute Leukemia Diagnosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To develop and test a web-based Clinical Decision Support System (CDSS) tool, which integrated a new fuzzy multiple criteria classification methodology named PROAFTN in acute leukemia (AL) diagnosis. METHODS: We have integrated a PROAFTN method and developed a web-based clinical decision support system using standard JSP, servlets, and XML technologies. All website data are database-driven; and the database system can handle data store, update, and retrieval instantly. Since the system was moved to a web server, we have started our experimental testing on 191 AL cases. RESULTS: The percentage of correct classification in this experimental testing was consistent with the proposed prototype. 96.4% of AL cases were correctly classified, proving that web-integration can be a promising tool for dissemination of CDSS tools. We found our system to be robust and capable of deployment for referring physicians. CONCLUSIONS: Our experimental results suggest that the Internet has promise as a means for distribution of CDSS tools. This system will help to: 1) make a "virtual" diagnosis and to compare its performances with given clinical diagnosis; 2) exchange health information between physicians and hematologists at the location and time of need; 3) assist online learning and simulate cases for training practitioners; 4) implement a strict security and access control for transmission of electronic health data through the Internet. The method will not replace specialists, but was developed to assist biologist-hematologists and general practitioners remotely in making decisions on medical diagnosis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it