Bibliographic record
Abstract
Y ou are called to the Emergency Department to assess a 63-year-old man with acute onset of shortness of breath.He has no known health conditions but has a 40 pack-year smoking history.He thinks that he may have the flu since his wife was just recovering from flu-like symptoms.He has not seen a healthcare provider in quite some time and this is his first visit to the hospital.Following some initial workup and imaging which showed a collapsed right lung, a chest CT scan was ordered which revealed metastatic lung cancer.As the physician, how would you approach informing the patient of his diagnosis?Bad news can be defined as any information that can drastically and negatively change a person's expectations or views about their future. 1While typical examples of bad news in the medical context include the diagnosis of terminal illness, it is important to step back and consider a wide spectrum of physical, emotional, social, and occupational factors that may impact a patient and thus could be considered bad news for that individual or their family. 1reaking bad news is a difficult and complex communication skill to acquire yet one that is essential for physicians.How bad news is delivered can have tremendous implications not just for patients and their families, but also for the physician.Developing this communication skill requires practice, self-reflection, and flexibility to adapt one's approach according to a given situation as well as to patient preferences, behavior, and understanding.While the focus of this article is on physicians, we acknowledge that other health care professionals are also frequently involved in such discussions and hence may also benefit from this article. 2
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 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".