Bibliographic record
Abstract
I read with interest the perspective of a foundation doctor on ‘breaking good news’ in the context of ‘negative’ findings’.1 The UK foundation programme comprises 2 years of workplace-based learning for newly qualified doctors, bridging the gap between university and specialist training. I am encouraged to hear that the programme allows graduates to participate in challenging consultations, including those addressing diagnostic uncertainty. As a fifth-year medical student at the University of Cambridge, my peers and I find it difficult to discuss ‘negative’ test results when, despite investigations, we are unable to give patients a specific diagnosis. We fear that our inability to explain causality may be perceived as failure, by both clinicians and patients. Within our curriculum we receive interactive teaching on ‘Clinical Communication Skills’, covering themes underpinned by the Calgary–Cambridge Guide.2 When considering diagnostic uncertainty in clinical practice, our ‘Explanation and planning’ sessions have been especially useful. These facilitate discussions of complex and undetermined diagnoses with actors role-playing concerned patients. Beyond these simulations, we are encouraged to seek challenging consultations in clinical environments. This is achieved through conversations with senior clinicians, communicating our willingness to consult with patients who have unclear diagnoses. Such interactions are observed, allowing for feedback. Post-consultation reflection also facilitates the consideration of over-investigation and the potential for iatrogenic harm.3, 4 Discussions surrounding diagnostic uncertainty remain difficult for students and clinicians. I feel that clinical educators and students must endeavour to identify opportunities for building confidence in these conversations, within formal teaching environments and clinical placements.
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 | 0.016 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.023 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.031 | 0.060 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".