Bibliographic record
Abstract
Some technologies, like scissors and chopsticks, appear inherently simple. Others, like nuclear reactors or life-support electronics, are inherently complex. By nature, the safety issues associated with complex systems are more involved than those associated with simple systems. There are always added cost requirements in complexity, such as special requirements for ensuring safe operation of the system. The very subsystems added to increase safety, however, necessarily add to complexity and, ironically, enrich the number of possible failure modes in the overall system. Thus there is a concern that the failure of any additional safety system may itself lead to new system failure modes that would not have otherwise occurred [1]. These comments explain why simply adding complexity to a system may not always improve on the system’s performance. The following illustrates some of the concerns that I have developed about the potential misapplication of computers in medical technology. These concerns are based on my clinical experience over the last decade in dealing with high-tech aspects of medical care.
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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.009 | 0.024 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".