Examples of dramatic failures and their effectiveness in modern surgical disciplines: can we learn from our mistakes?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Innovation can be variably defined, but when applied to healthcare is often considered to be the introduction of something new, whether an idea, method or device, into an unfilled void or needy environment. Despite the introduction of many positive surgical subspecialty altering concepts/devices however, epic failures are not uncommon. These failures can be dramatic in regards to both their human and economic costs. They can also be very public or more quiet in nature. As surgical leaders in our communities and advocates for patient safety and outcomes, it remains crucial that we meet new introductions in technology and patient care with a measured level of curiosity, skepticism and science-based conclusions. The aim of an expert committee was to identify the most dominant failures in technological innovation and/or dogmatic clinical beliefs within each major surgical subspecialty. In summary, this effort was pursued to highlight the past failures and remind surgeons to remain vigilant and appropriately skeptical with regard to the introduction of new innovations and clinical beliefs within our craft.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it