Bibliographic record
Abstract
The demise of anatomy teaching in the undergraduate medical curriculum has inevitably reduced the general level of applied anatomical knowledge displayed by junior doctors. Initiatives such as the European Working Time Directive have exacerbated the problem by reducing trainees' opportunities to acquire appropriate anatomical knowledge and clinical skills through workplace training. Medical Schools and postgraduate Colleges and Schools of Surgery must work together to design and deliver quality-assured courses in core and non-core anatomy, that cross the undergraduate/postgraduate interface. All medical students should learn a core syllabus of anatomy, agreed by a panel of clinicians and anatomists but delivered according to the pedagogic style favoured by individual Medical Schools. This core will define the anatomy, that all F1 doctors should know, particularly the anatomy associated with clinical procedures: it will be assessed across all years of the undergraduate medical programme. Medical Schools should also offer modules in non-core surgical and/or radiological anatomy, some of which may be designed and delivered in partnership with Colleges of Surgery and Radiology: these modules would be particularly attractive to students contemplating a career in surgery or interventional radiology, but would not be offered exclusively to this cohort. At present, the inadequate anatomical knowledge of Foundation doctors must be addressed by ensuring that early postgraduate training programmes include explicit, formal teaching in anatomy, for example, the Core Surgical Anatomy course currently being piloted at the Royal College of Surgeons of England.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.017 |
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".