Technological Change in Modern Surgery
Bibliographic record
Abstract
Examining the complex dynamics of medical treatment options and the variable character of surgical technologies, this volume broadens and transcends the notion of technological innovation. Surgery is an ideal field for examining the processes of technological change in medicine. The contributors to this book go beyond the concept of innovation, with its focus on a single technology and its sharp dichotomy of acceptance versus rejection. Instead they explore the historical contexts of change in surgery, looking at the complex dynamics of the various treatment options available -- old and new, surgical and nonsurgical -- as well as the variable character of the new technologies themselves, thus broadening and transcending the notion of technological innovation. CONTRIBUTORS: Christopher Crenner, Sally Frampton, Delia Gavrus, Lisa Haushofer, David S. Jones, Beth Linker, Shelley McKellar, Thomas Schlich Thomas Schlich is the James McGill Professor of the History of Medicine at the Department of Social Studies of Medicine at McGill University. Christopher Crenner is the RalphMajor and Robert Hudson Professor and chair of the Department of History and Philosophy of Medicine at the University of Kansas Medical Center.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".