Barriers to Quality End-of-Life Care for Patients With Blood Cancers
Bibliographic record
Abstract
PURPOSE: Patients with blood cancers have been shown to receive suboptimal care at the end of life (EOL) when assessed with standard oncology quality measures (eg, no chemotherapy ≤ 14 days before death). As they were developed primarily for solid tumors, it is unclear if these measures are appropriate for patients with hematologic malignancies. Moreover, barriers to high-quality EOL care for this specific patient population are largely unknown. METHODS: In 2015, we asked a national cohort of hematologic oncologists about the acceptability of eight standard EOL quality measures. Building on prior qualitative work, we prespecified that measures achieving agreement among at least 55% of respondents would be considered acceptable. We also explored perspectives regarding barriers to quality EOL care. RESULTS: We received 349 surveys (response rate = 57.3%). Six of the standard measures met the threshold of acceptability, and four were acceptable to > 75% of respondents: hospice admission > 7 days before death, no chemotherapy ≤ 14 days before death, no intubation in the last 30 days of life, and no cardiopulmonary resuscitation in the last 30 days of life. The highest-ranked barriers to quality EOL care reported were "unrealistic patient expectations" (97.3%), "clinician concern about taking away hope" (71.3%), and "unrealistic clinician expectations" (59.0%). CONCLUSION: In this large national cohort of hematologic oncologists, standard EOL quality measures were highly acceptable. The top barrier to quality EOL care reported was unrealistic patient expectations, which may be best addressed with more timely and effective advance care discussions.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".