Utilization of POCUS in a Specialist Palliative Care Setting: A Retrospective Chart Review.
Bibliographic record
Abstract
Abstract Background: The use of Point-Of-Care Ultrasound (POCUS) has increased rapidly across various medical disciplines due to technological advancements providing high quality POCUS units. POCUS can help clinicians at the bed side with information regarding patient management in real time. However, literature reveals scant evidence of POCUS use in Palliative Care. This study’s objective was to examine the use of POCUS in a specialist palliative care setting. Methods: A retrospective chart review was conducted from January 2018 to June 2019 to evaluate characteristics of patients for whom POCUS was utilized. These patients were identified through pre-existing logs and descriptive information was collected from the electronic health records. This included demographic information, life-limiting diagnosis, patient assessment location, diagnosis made with POCUS and, if applicable, volume of fluid drained.Results: We identified 126 uses of POCUS in 89 unique patients. 62 patients (69.7%) had a cancer diagnosis, with patients most commonly suffering from GI, Lung and Breast pathologies. 61 POCUS cases (48.4%) were in the outpatient setting. 81 POCUS cases (64.3%) revealed a diagnosis of ascites and 21 POCUS cases (16.7%) revealed a diagnosis of pleural effusion. Other diagnoses made with POCUS included bowel obstruction, pneumonia and congestive heart failure. During the study period, 52 paracentesis and 7 thoracentesis procedures were performed using POCUS guidance.Conclusion: We identified multiple indications in our specialist palliative care setting where POCUS aided in diagnosis/management of patients in both inpatient and outpatient settings. Further studies can be conducted to identify the potential benefits in symptom burden, patient & caregiver satisfaction and health care utilization in palliative care patients receiving POCUS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".