What Happens to Unused Symptom Response Kits in the Community: A Narrative Review
Bibliographic record
Abstract
Introduction:It is a common practice in Ontario to prescribe patients with advanced life-limiting illness a Symptom Response Kit (SRK), an emergency medication and medical supply kit, to manage distressing symptoms in the community toward the end of life (EOL). These are hugely beneficial to patients; however, the medications included have high risk profiles, and there is no uniform procedure in place for the return or disposal of SRKs. This narrative review aims to identify what happens to unused SRKs when no longer needed. Methodology:The literature search was conducted on Medline Ovid, Embase, and CINAHL databases in early 2022 and re-conducted from 2022 to September 15, 2023. Ontario Health at Home (OHAH) websites were also searched in early 2022 and again in January 2024. We included conference abstracts, systematic reviews, scoping reviews, and clinical trials published in the English language that discussed the use of SRK (or equivalent) in community palliative care. Results:Twenty-six studies were included, 12 (46.2%) of which originated from the United States and 3 (11.5%) from Canada. Few studies (34.6%) discussed safety. One study provided instructions given to patients and families regarding return or disposal of unused kits. There was no consistent terminology for SRKs in studies or throughout OHAH organization websites. Three OHAH organizations’ websites had instructions available on procedures for disposal or return of unused kits. Conclusions:There is a paucity of research on the use of SRK, although the practice is common. There is no consensus on SRK terminology and no studies evaluating the return or safe disposal of SRKs in the community when no longer needed. Future work should establish safety regulations, disposal monitoring, supervision of use, and terminology standardization. OHAH organizations provincially are conducting innovative work in this area.
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 | 0.014 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| 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".