Tele-clinics in palliative care during the COVID-19 outbreak
Bibliographic record
Abstract
OBJECTIVES: To investigate the effect of tele-clinics on palliative care patients during the COVID-19 pandemic. METHODS: This is a retrospective cross-sectional study (chart review) carried out from March 17, 2020, to September 16, 2020, included all patients who were booked into the palliative care clinic. Patients were assessed by the palliative nurse specialist for COVID-19 symptoms using the acute respiratory illness screening form and Edmonton Symptoms Assessment System, also identifies the needs of the patient. Data were analyzed to investigate the effect of tele-clinics on the patients regarding ER visits and admission. RESULTS: A total of 167 individuals were analyzed and the results showed that 234 of 447 visits were virtual, supporting the increasing value of telemedicine. The number of virtual patients' visits dropped slightly at the beginning of the pandemic (46.4% in March to 39.8% in July). Subsequently, it increased steadily to 72.2% in September. The choice of virtual/non-virtual visits for individuals with cancer diagnosis significantly depends on other factors. Code status, palliative patients or follow-up service, and the frequency of oncology center visits, admissions, or ER visits were crucial in explaining the means of receiving treatment. CONCLUSION: Virtual visits in palliative care are efficient means of decreasing the threat of COVID-19 contagion. It is recommended to increase the palliative care patients' awareness of tele-clinics and their positive outcomes, particularly during the pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".