Interim Analysis of Attrition Rates in Palliative Care Study on Dignity Therapy
Bibliographic record
Abstract
A routine threat to palliative care research is participants not completing studies. The purpose of this analysis was to quantify attrition rates mid-way through a palliative care study on Dignity Therapy and describe the reasons cited for attrition. Enrolled in the study were a total of 365 outpatients with cancer who were receiving outpatient specialty palliative care (mean age 66.7 ± 7.3 years, 56% female, 72% White, 22% Black, 6% other race/ethnicity). These participants completed an initial screening for cognitive status, performance status, physical distress, and spiritual distress. There were 76 eligible participants who did not complete the study (58% female, mean age 67.9 ± 7.3 years, 76% White, 17% Black, and 7% other race). Of those not completing the study, the average scores were 74.5 ± 11.7 on the Palliative Performance Scale and 28.3 ± 1.5 on the Mini-Mental Status Examination, whereas 22% had high spiritual distress scores and 45% had high physical distress scores. The most common reason for attrition was death/decline of health (47%), followed by patient withdrawal from the study (21%), and patient lost to follow-up (21%). The overall attrition rate was 24% and within the a priori projected attrition rate of 20%-30%. Considering the current historical context, this interim analysis is important because it will serve as baseline data on attrition prior to the outbreak of the COVID-19 pandemic. Future research will compare these results with attrition throughout the rest of the study, allowing analysis of the effect of the COVID-19 pandemic on the study attrition.
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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 | 0.087 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".