Palliative medicine outpatient clinic ‘no-shows’: retrospective review
Bibliographic record
Abstract
OBJECTIVES: Patients who do not attend outpatient palliative care clinic appointments ('no-shows') may have unmet needs and can impact wait times. We aimed to describe the characteristics and outcomes associated with no-shows. METHODS: We retrospectively reviewed new no-show referrals to the Princess Margaret Cancer Centre Oncology Palliative Care Clinic (OPCC) in Toronto, Canada, between January 2017 and December 2018, compared with a random selection of patients who attended their first appointment, in a 1:2 ratio. We collected patient information, symptoms, performance status (Eastern Cooperative Oncology Group (ECOG) and outcomes. Univariable and multivariable logistic regression analyses were used to identify significant factors. RESULTS: Compared with those who attended (n=214), no-shows (n=103), on multivariable analysis, were at higher odds than those who attended of being younger (OR 0.98, 95% CI 0.96 to 1.00, p=0.019), living outside Toronto (OR 2.67, 95% CI 1.54 to 4.62, p<0.001) and having ECOG ≥2 (OR 2.98, 95% CI 1.41 to 6.29, p=0.004). No-shows had a shorter median survival compared with those who attended their first appointment (2.3 vs 8.7 months, p<0.001). CONCLUSION: Compared with patients who attended, no-shows lived further from the OPCC, were younger, and had a poorer ECOG. Strategies such as virtual visits should be explored to reduce no-shows and enable attendance at OPCCs.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".