Bibliographic record
Abstract
Revalidation or the process by which a surgeon demonstrates their right to practice has long been established in the United States and Canada and is currently being introduced in the United Kingdom. Its primary purpose is to demonstrate that surgeons continue to meet the standards that apply in their discipline. Secondary purposes are to promote continuing professional development, encourage improvement in the quality of healthcare and the identification of surgeons for whom there are significant concerns about their fitness to practice and to alert for early signs of deteriorating performance. Finally it is to reassure the public, colleagues and employers that individual surgeons are up to date and fir to practice. Although there are different methods for undertaking revalidation, experiences on the use of self‐regulation have shown that it can be effective and maintain the public trust. This method would seem preferable to the development of a testing culture based on summative examinations. Important principles for revalidation include the College and specialty associations setting of standards and the evidence required and the importance of surgeons gathering the evidence in their personal portfolio. The process should be locally based and include a responsible person who can provide assurance of an individual's continued fitness to practice. The College and specialty associations must provide support and advice to surgeons going through the process. A number of criteria are used including professional standing, evidence of lifelong learning and up to date clinical knowledge primarily through self‐directed learning and self‐assessment. Evaluation of performance in practice can be drawn from outcome data, patient feedback and observations of practice and simulator tests.
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 | 0.320 | 0.505 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.011 | 0.025 |
| Research integrity | 0.027 | 0.023 |
| Insufficient payload (model declined to judge) | 0.012 | 0.015 |
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".