Development and Pilot Implementation of a Patient Oriented Discharge Summary for Critically Ill Patients (PODS-ICU)
Bibliographic record
Abstract
Abstract Background - Patients leaving the intensive care unit (ICU) often experience gaps in care due to deficiencies in discharge communication. This study aimed to develop an ICU specific patient-oriented discharge summary tool (PODS-ICU) and pilot test the tool for acceptability and feasibility.Methods - Patient-partners, ICU clinicians, and researchers met to discuss ICU patients’ specific informational needs and design the PODS-ICU through several cycles of iterative revisions. Research team nurses piloted the PODS-ICU with patient and family-caregiver participants in two ICUs in Calgary, Canada. Follow-up surveys on the PODS-ICU and its impact on discharge were administered to participants and ICU nurses.Results – Fifteen patient and family-caregiver participants were administered the PODS-ICU. Most participants felt that their discharge from the ICU was good or better (n=13), and some (n=9) participants reported a good understanding of why the patient was in ICU. Most participants (n=12) reported that they understood ICU events and impacts on the patient’s health. ICU nurses reported that the PODS-ICU was “not reasonable” in their daily clinical workflow due to “time constraint”. Conclusions - PODS-ICU improves patients and family-caregivers’ understanding of ICU events and health-implications but requires better integration with existing care processes to be feasible. Patient or Public Contribution – This work involved patient partners (i.e., individuals with lived experience as patients or family-caregivers) in tool development, study design, participant recruitment, and manuscript preparation.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".