Improving Access and Timeliness of Early Palliative Care Specialist Assessment for Patients With Advanced Lung Cancer in a Rapid Assessment Clinic
Bibliographic record
Abstract
Background: Integrating palliative care in the management of patients with lung cancer improves quality of life, patient satisfaction, and overall survival. However, few patients receive timely palliative care consultation. The Lung Diagnostic Assessment Program (LDAP) in Southeastern Ontario is a multidisciplinary rapid assessment clinic that expedites the diagnosis and management of patients with suspected lung cancer. Objectives: We sought to increase the percentage of LDAP patients with stage IV lung cancer receiving palliative care consultation within three months of diagnosis. Design: We integrated a palliative care specialist in LDAP to facilitate in-person, same-visit consultation for patients with a new lung cancer diagnosis. Setting/Subjects: Five hundred fifty patients in a Canadian academic center (154 initial baseline, 104 COVID baseline, 292 post-palliative care integration). Measurements: Baseline data were established using retrospective chart review (February–June 2020 and December 2020–March 2021 due to COVID-19 pandemic). Data were collected prospectively to assess improvement (March–August 2021). Statistical Process Control charts assessed for special cause variation; chi-square tests assessed for differences between groups. Results: The percentage of patients with stage IV lung cancer seen by palliative care within three months increased from 21.8% (12/55) during early-COVID baseline to 49.2% (32/65) after palliative care integration (p < 0.006). Palliative care integration in LDAP reduced mean time from referral to consultation from 24.8 to 12.3 days, including same-day consultation for 15/32 (46.8%) patients with stage IV disease. Conclusions: Integrating palliative care specialists into LDAP improved the timeliness of palliative care assessment for patients with stage IV lung cancer.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".