Attitude and support to inform clinical predictions of survival in patients with advanced cancer.
Bibliographic record
Abstract
39 Background: Tumour factors (eg primary site, size) helpful in prognostication in early stage cancer may be less important than patient factors, such as Karnofsky performance status (KPS), later in the disease trajectory. We evaluated the utilization of the parameters attitude (ATT) and psychosocial support (PSS) in predicting remaining lifespan by two experienced multidisciplinary teams (MDT). Methods: After clinical assessment of consecutive patients, a quantitative clinical prediction of survival (CPS) was made prospectively, independently and anonymously by each health care provider (HCP). HCP recorded up to 4 factors (free-text) influencing their CPS. The Palliative Radiation Oncology (06/2010-12/2014) and Palliative Care (06/2012-01/2014) teams participated. Demographics, disease information, and date of death were abstracted. Summary statistics (means, standard deviations, and proportions) were calculated. Estimates for actual survival (AS) were obtained. Wilcoxon tests compared continuous variables and chi-square tests were used for categorical variables. Results: 2,582 predictions were made by 11 disciplines for 881 unique patients over 1,054 clinic visits. Median age was 66 years, 58.6% were male and 37.3% had lung cancer. KPS, when recorded, was ≥ 70 at 46.3% of visits. Cognitive status was below expected for 174/460 visits. Mean AS was 177 ± 190 days and mean CPS was 258 ± 300 days. The median number of factors cited was 4 (range 0-4), with disease extent most common. ATT and PSS as perceived by the HCP influenced 6.7% and 5.8% of CPS respectively, varying across disciplines (p < 0.0001). Overall, mean CPS was significantly longer when ATT (337d vs 252d; p < 0.0001) or PSS (310d vs 254d; p < 0.004) was quoted. CPS estimates by Nursing and Students were longer when ATT was mentioned (both p < 0.004). Students’ CPS were longer when PSS was described (p = 0.048). Mean AS was also longer when ATT (259d vs 178d; p < 0.0001) or PSS (224d vs 181d; p = 0.03) were cited overall, as well as within the Nursing, Student and Pharmacist groups (all p < 0.05). Conclusions: Attitude and available psychosocial support as evaluated by MDT appeared to correlate with both clinical prediction of survival and actual survival in this cohort.
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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 | 0.002 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".