5th international workshop on software engineering in health care (SEHC 2013)
Bibliographic record
Abstract
Our ability to deliver timely, effective and cost efficient healthcare services remains one of the worlds foremost challenges. The challenge has numerous dimensions including: (a) the need to develop a highly functional yet secure electronic health record system that integrates a multitude of incompatible existing systems, (b) in-home patient support systems to reduce demand on professional health-care facilities, and (c) innovative technical devices such as advanced pacemakers that support other healthcare procedures. Responding to this challenge will necessitate increased development and usage of software-intensive systems in all aspects of healthcare services. However the increased digitization of healthcare has identified extensive requirements related to the development, use, evolution, and integration of health software in areas such as the volume and dependability of software required, and the safety and security of the associated devices. The goal of the fifth workshop on Software Engineering for Health Care (SEHC) is to discuss recent research innovations and to continue developing an interdisciplinary community to develop a research, educational and industrial agenda for supporting software engineering in the health care sector.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.016 |
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".