Assessing quality end-of-life communication and documentation in intensive care patients using a conceptual framework and quality indicators
Bibliographic record
Abstract
Most deaths in Canada occur in hospitals, and almost one in five occurs in intensive care units (ICUs). The goal of this study is to assess the quality of end-of-life (EOL) communication in two important groups in intensive care in Winnipeg: (i) those who live in personal care homes (PCH) and (ii) those with severe cardiovascular and/or respiratory failure placed on an artificial life support called extracorporeal membrane oxygenation (ECMO). Two domains of EOL communication were studied: Goals of Care Discussion (GOCD) and Documentation. We used a validated conceptual framework for the quality of EOL communication and documentation, operationalized by 18 specific quality indicators (QIs). We performed a retrospective, manual review of hospital charts (107 charts from the PCH subgroup and 103 charts from the ECMO subgroup) to extract these QIs. Overall, the quality of EOL communication and documentation was low. Despite the ECMO cohort being the sicker group with worse in-hospital mortality rates, the quality of EOL communication was significantly worse compared to PCH group. Quality of EOL communication was highly influenced by patient physiologic status adjusted for age, sex, year of admission, disease category, socioeconomic quintile and urban status.
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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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".