What is the impact of specialist palliative care outpatient consultations on pain in adult patients with cancer? A systematic review
Bibliographic record
Abstract
PURPOSE: The aim of this systematic review was to determine the impact of specialist palliative care (SPC) consultations in outpatient settings on pain control in adults suffering from cancer. METHODS: Systematic Review. Databases CINAHL, Medline, PsychInfo, and Embase were searched in February 2021. Relevant studies were also hand-searched and gray literature was searched in February 2021. The PICO mnemonic (Population, Intervention, Comparison, and Outcome) was used to form the review question. Of 1053 potential studies identified, 10 met the inclusion criteria. Quality appraisal of included studies was conducted using the evidence-based librarian (EBL) critical appraisal checklist. RESULTS: Outcome data from 56% (n = 5/9) studies indicated a non-statistically significant reduction in pain. Narrative analysis of the remaining studies indicated a statistically significant reduction in pain in 50% (n = 2/4) of the studies, one study showed mixed results, and one study found no statistically significant improvement in pain control. In relation to secondary outcomes, results from 33% (3/9) of studies indicated statistically significant improvement in symptom control. Data from 22% (n = 2/9) of studies indicated no statistically significant improvement in the symptoms measured. Narrative analysis of the remaining four studies indicated generally mixed results. EBL scores of included studies ranged between 50% and 95.23%. CONCLUSION: Outpatient SPC consultations may have a positive impact on the control of pain and other distressing symptoms for cancer patients, however, results show mixed effects. Given that it is unclear what it is about outpatient SPC that impacts positively or otherwise on pain and symptom control.
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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 | 0.011 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".