Telemedicine in palliative care: a review of systematic reviews.
Bibliographic record
Abstract
AIMS: To evaluate the quality of systematic reviews on telemedicine applications in palliative care. METHODS: A structured literature review was conducted to identify systematic reviews dealing with telemedicine in palliative care; the AMSTAR (Assessment of Multiple Systematic Reviews) checklist was used to appraise the evidence related to the systematic reviews. RESULTS: 405 records were initially identified; of these 14 were eligible for full-text analysis. In summary, the research strategy allowed the identification of 6 reviews to be included which showed a medium quality (AMSTAR score in between 4 and 7). All the included systematic reviews considered telemedicine applications as a feasible means to be used in palliative care; however, the positive findings are counterbalanced by several critical issues mainly related to the evidence from the primary studies included in each single review. CONCLUSIONS: Results of this first attempt to appraise the evidence in the field of telemedicine applications in palliative care highlighted that there is still limited evidence related to this approach. Strengths and weaknesses that impact on the general quality of the reviews were identified and relevant points to be taken into account for future research were suggested.
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 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 | 0.022 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".