Referral Patterns of Nonmalignant Patients to an Irish Specialist Palliative Medicine Service
Bibliographic record
Abstract
BACKGROUND: Our perception is that the proportion of referrals made to the specialist palliative medicine service (SPMS) in our institution for patients with a primary diagnosis of nonmalignant disease is high and that these patients are often referred late in their illness. We aimed to review the symptom burden and referral patterns of patients with a noncancer diagnosis to the SPMS in our centre. METHODS: All new non-malignant referrals to the SPMS in 2009 were included. Data were collected from patients' medical records and analyzed using Excel. RESULTS: Ninety-two referrals were identified: 60 (65%) female, 32 (35%) male. Mean age 76.5 years (21-92). Reasons for referral included: end-of-life care (n=55, 60%), symptom control (n=23, 25%), home care support (n=13, 14%) and psychological support (n=1, 1%). Mean time from admission to referral was 24.9 days (<1-165). Fifty-six (61%) patients were commenced on a syringe driver (CSCI), with a mean time spent on a CSCI of 2.8 days (< 1-17). Primary outcomes included: death (n=72, 78.5%), home discharge (n=9, 10%), discharge to another care institution (n=6, 6.5%), discharge from service (n=3, 3%) and hospice transfer (n=2, 2%). Mean time from referral to outcome was 4.6 days (<1-35). CONCLUSION: The proportion of noncancer patients referred to the SPMS is our institution is high. This study confirms that nonmalignant referrals are commonly sent to the SPMS when patients are actively dying or very imminently dying. Further education of colleagues is warranted in the role of the SPMS, particularly with regard to earlier referral.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".