THE APPLICATION OF OPEN SOURCE SOFTWARE IN HEALTH: A SCOPING REVIEW OF VALIDATED SOFTWARE
Bibliographic record
Abstract
IT is recognized as a catalyst for higher efficiency and better performance in health organizations. However, due to financial constraints and the expensive cost of commercial solutions, the adoption of health and medical informatics (HMI) has lagged behind expectations. Open source software (OSS) appears as an alternative to reduce the barriers of HMI adoption and challenge the commercial status quo. There is a wide variety of available programs with a wide variety of features; however, unlike drugs and medical treatment devices, OSS developed for health purposes are not required to be clinically validated in a trial. There are numerous sources on the Internet and studies that propose lists of OSS in health care; however, none of them have considered the impact of the selected OSS or the evaluation of the software quality. This scoping review permitted to identify 25 validated OSS applications in numerous health fields, such as radiology, neurology, cardiology and surgery. Moreover, the scoping review permitted to portray the validation process and to apply, for the first time, the D&M model on OSS in health.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".