Using technology to accurately capture functional outcomes in sarcoma patients
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
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.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it