PROgnostic Model for Advanced Cancer (PRO-MAC)
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a simple prognostic tool for early prediction of survival of patients with advanced cancer in a tertiary care setting. DESIGN: Prospective cohort study with 2 years' follow-up. SETTING: Single tertiary teaching hospital in Singapore. PARTICIPANTS: The study includes consecutive patients diagnosed with advanced cancer who were referred to a palliative care unit between 2013 and 2015 (N=840). Data were randomly split into training (n=560) and validation (n=280) sets. RESULTS: 743 (88.5%) patients died with a mean follow-up of 97.0 days (SD 174.0). Cox regression modelling was used to build a prognostic model, cross-validating with six randomly split dataset pairs. Predictor variables for the model included functional status (Palliative Performance Scale, PPS V.2), symptoms (Edmonton Symptom Assessment System, ESASr), clinical assessment (eg, the number of organ systems with metastasis, serum albumin and total white cell count level) and patient demographics. The area under the receiver operating characteristic curve using the final averaged prognostic model was between 0.69 and 0.75. Our model classified patients into three prognostic groups, with a median survival of 79.0 days (IQR 175.0) for the low-risk group (0-1.5 points), 42.0 days (IQR 75.0) for the medium-risk group (2.0-5.5 points), and 15.0 days (IQR 28.0) for the high-risk group (6.0-10.5 points). CONCLUSIONS: PROgnostic Model for Advanced Cancer (PRO-MAC) takes into account patient and disease-related factors and identify high-risk patients with 90-day mortality. PPS V.2 and ESASr are important predictors. PRO-MAC will help physicians identify patients earlier for supportive care, facilitating multidisciplinary, shared decision-making.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".