QOLP-18. A TIME-BASED MODEL OF EARLY PALLIATIVE CARE INTERVENTION IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA, A SINGLE INSTITUTION FEASIBILITY STUDY
Bibliographic record
Abstract
Abstract OBJECTIVE There is no validated model for delivering palliative care (PC) in the glioblastoma (GBM) population. The primary objectives were to assess the feasibility and determine the acceptability of a time-based model of integrated specialty PC to patients and providers. Secondary objectives were to estimate the impact on healthcare utilization and quality of life (QoL) compared to historical controls. METHODS We consented and referred patients to PC at their initial Neuro-Oncology consultation between 4/2018 and 5/2019. We conducted QoL assessments (NCCN Distress Tool; Functional Assessment of Cancer Therapy-Brain (FACT-BR); Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F); Epworth Sleepiness Scale (ESS)) at (1) baseline (2) immediately after chemoradiation, and (3) 6 months following chemoradiation. Ongoing PC follow-up was at the discretion of the PC provider. We administered the Edmonton Symptom Assessment System (ESAS) before and after PC visits. We measured patient and referring provider satisfaction using FAMCARE-16 and a PC departmental survey, respectively. RESULTS We did not meet our goal enrollment of 50 patients. 32 were offered participation, 12 consented and 8 attended at least one PC visit. The mean number of PC visits was 1.6. Mean age was 62 (42–79). 75% had a KPS ≥80. Of those that did not complete the study, 2 died and 5 either withdrew consent or declined further visits. At baseline, 91.7 % had a NCCN distress score ≥4. Patients were overall satisfied with the intervention. CONCLUSION Introduction of specialty PC at the time of GBM diagnosis is challenging. Participants reported their experience as overall positive. Results from referring providers are pending. Due to low-enrollment we did not pursue further statistical comparisons regarding healthcare utilization compared to historical controls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".