Distinguishing symptom patterns in adults newly diagnosed with cancer: a latent class analysis
Bibliographic record
Abstract
CONTEXT: Socio-demographic differences, including place of residence, socio-economic status, ethnicity, and gender, have been associated with various inequities in cancer care outcomes. OBJECTIVES: The aims were to distinguish subgroups of patients with different symptom patterns at the time of the initial oncology visit and determine which clinical and socio-demographic variables are associated the different symptom patterns. METHOD: Responses to the Edmonton Symptom Assessment Scale- revised and clinical and socio-demographic variables were obtained via the Ontario Cancer Registry and linked health data files. Latent class analyses were conducted to identify and compare the subgroups. RESULTS: The cohort (n = 216,110) with a mean age of 64.5 years consisted of 54.1% women. The analyses identified six latent classes (proportions ranging from 0.09 to 0.31) with distinct symptom patterns, including: 1) many severe symptoms, 2) many less severe symptoms, 3) predominantly mild symptoms, 4) severe psychosocial symptoms, 5) severe somatic symptoms, 6) few symptoms. The subgroups were associated not only with clinical differences (diagnoses and functional status), but also with various socio-demographic (age, sex) and community characteristics (neighborhood income, proportion of foreign born, rurality). CONCLUSION: The results indicated that there were substantial differences in symptom patterns at the time of the initial oncology visit, which were associated with both clinical diagnoses and socio-demographic differences. These results point to the importance of taking the social situation of patients into account, and not just diagnosis, to better understand differences in symptom patterns of people living with cancer.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".