Does gynecologic malignancy predict likelihood of a tertiary palliative care unit hospital admission? A comparison of local, provincial and national death rates
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine whether the presence of gynecologic malignancies predicts the likelihood of a tertiary palliative care unit hospital admission. METHOD: In this study, patients admitted to a specialized tertiary palliative care unit (TPCU) with gynecologic malignancies were compared to national and provincial death rates to determine if gynecologic malignancy predicts admission, and subsequent death, in a TPCU. RESULTS: Eighty-two gynecologic cancer patients were admitted to our TPCU over the 5- year study period. Out of all cancer deaths in the TPCU, death from ovarian cancer was 3.7% compared with 2.4% (p = 0.0068) of all cancer deaths in Manitoba and 2.3% (p = 0.0043) of all cancer deaths in Canada. Cervical cancer accounted for 1.7% of all our patients deaths compared with 0.7% (p = 0.0001) provincially and 0.6% (p = 0.0001) nationally. Uterine cancer deaths were not significantly different from the provincial and national death rates, whereas vulvar and fallopian cancers were too rare to allow for statistical analysis. SIGNIFICANCE OF RESULTS: Gynecologic cancers may be predictive of admission to a palliative care unit.
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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 | 0.006 |
| 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) | 0.002 | 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".