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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".