The PULSES project: Teaching the vital elements of code status discussions to oncology residents.
Bibliographic record
Abstract
46 Background: Discussions with cancer patients around cardiopulmonary resuscitation, or ‘code status,’ are often led by trainees in oncology, but formal education for this competency is lacking. In this study, we developed and tested a novel communication tool, the PULSES framework, for informed code status decision-making (a six-step approach summarized by the PULSES acronym [see Table]), through an educational workshop. Methods: A multicentre randomized controlled trial was carried out at 3 academic cancer centres in Ontario, Canada. Residents in medical oncology (MO) and radiation oncology (RO) programs completed a workshop and an observed structured clinical exam (OSCE). Participants were randomized to complete the training before the OSCE (experimental arm) or after the OSCE (control arm). Randomization was stratified for centre and oncology discipline. Expert raters evaluated communication with two rating tools: the novel PULSES scale and the communication skills assessment form (CSAF), a validated benchmark tool that is not specific to oncology content. The primary outcome was improvement in PULSES scores. Results: Forty-six residents consented to participate (28 RO and 18 MO). Groups were well balanced for program and year of training. Participants in the experimental group had higher mean PULSES score than those in the control group (80.4±13.5 vs 63.4±9.7; p<.001; maximum score = 108). There was no significant effect for oncology program and no significant interaction between program and training condition. Scores from the PULSES and CSAF scales were highly correlated (R = 0.864). Conclusions: The PULSES training improved performance among oncology residents for code status discussions. Improved communication scores were not scale-specific. The PULSES framework offers a standardized approach and can be incorporated into competency-based curricula for postgraduate oncology programs. Future work will explore whether communication training in this area impacts patient-level outcomes. [Table: see text]
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".