A Structured Approach to History and Physical Examination in Oncology for Medical Learners
Bibliographic record
Abstract
In oncology, traditional H&P templates centered on a single chief complaint often fail to address the longitudinal care needs and emotional complexities of cancer patients, leaving learners unprepared for sensitive conversations such as breaking bad news or discussing treatment goals. To address this, we conducted a literature review of specialty-focused H&P tools in child psychiatry and gynecology and, drawing on our experiences as two first-year medical students in an outpatient oncology clinic, developed an oncology H&P template to guide novice clinicians. The guide incorporates structured prompts for rapport-building; detailed oncologic and family cancer history; functional independence assessments; treatment goals; emotional wellbeing; support networks; and responding to emotion. After initial pilot testing by the two developers under supervisor guidance, the template was distributed to five then ten additional students and disseminated via the ASCO online forum and Twitter. Feedback from ten oncologists and oncology trainees highlighted the template’s value in gathering review of systems, past treatment details, functional status, and cancer history. Our findings suggest that this oncology-tailored tool enhances interview flow, promotes comprehensive data collection, and supports empathetic patient engagement. Integration into routine oncology training is planned, with future adaptations for specific oncological subspecialties and potential use in other medical specialties.
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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".