Bibliographic record
Abstract
Health information and communication technology (HICT) poses technology specific hazards to patient safety. The FDA's MAUDE database is one source among many which reports on HICT related patient injuries and deaths. The stagnancy of the safety of these technologies in the ten years following Institute of Medicine warnings indicates a lack understanding of the nature of these hazards. As we have remedied the issues in existing technology, a pandemic of similar issues will soon be on us as more HICT will be deployed in the next ten years than has been in the history of medicine. To address this gap, we have adapted Leveson's work on socio-technical safety engineering to develop a system theoretic model of information systems that re-imagines them as traditional control systems. We call this model System Theoretic Accidents Models and Processes for Information Systems (STAMP-IS). We have incorporated the model into a systematic safety engineering process we call information system hazard analysis and mitigation (ISHAM). ISHAM consists of an iterative four step process which includes team selection, modelling, analysis, and mitigation. It requires a process under investigation (PUI) as input, and retrospective accident data (RAD) ideally about the PUI itself, though RAD about a substantially similar process can be substituted.
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.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".