Using technology to accurately capture functional outcomes in sarcoma patients
Bibliographic record
Abstract
The project is in collaboration with the Nottingham University Hospital (NHS) and focuses on capturing information to inform the evaluation of the functional outcomes following sarcoma treatment. The Toronto Extremity Salvage Score (TESS) is the actual survey exist and already commonly used within the NHS for monitoring and evaluating the physical function of individuals and group of patients who undergoing limb preservation surgery for tumors of the extremities over time and measuring change in function due to different therapeutic interventions (1). \n• Problem: the existing process of paper-based TESS survey implementation in NHS failed to achieve their intended purpose of utilizes the functional outcomes data to capture the useful information for the further evaluation. \n \n• Objectives: solve the exposed problem of functional outcome data (following sarcoma treatment) gathering and capturing in NHS, using technology to improve the current operation mechanism and pattern for data collecting and processing. Finally, accomplish digital data capture, analysis and visualization. \n \n• Methodology: agile software management method is used to management the entire project. The human computer interactive (HCI) knowledge mainly support on requirement gathering, the design of high usability application and high quality evaluation questionnaire. For the implementation part, android based TESS questionnaire app is achieved by JAVA language, and Android SDK and Eclipse as the development environment. The PC based database driven host application programs by Visual Studio 2012 compiler for C#, and MySQL database to store and retrieve data. \n \n• Achievements: an Android based tablet TESS questionnaire application realized accurately digital functional outcome data gathering and transfer, and a Windows PC based system achieved the transferred results reviewing, analyzing and visualization. The entire system is qualified to replace the current process used in NHS.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".