Physical and psychological symptom burden in patients newly diagnosed with solid organ cancers in a tertiary care centre in India.
Bibliographic record
Abstract
e24081 Background: Assessment of physical and psychological symptoms using patient-reported outcome (PRO) scales is an often overlooked aspect in oncological practice in resource constrained settings. We assessed the pattern of symptoms at diagnosis in solid organ cancers using an innovative smart device based delivery of the Edmonton Symptom Assessment Scale (ESAS). Methods: We assessed PROs using ESAS at diagnosis in adult patients diagnosed with solid organ cancers at a large tertiary care centre in New Delhi, India from September 2021 to January 2022. The ESAS was delivered to patient's smart phone as a text message that provided a unique link to complete the questionnaire in Hindi or English, as preferred by the patient. Symptom severity was categorized into physical, psychological, and total symptom domains and the scores were further classified as none to mild (0-3) or moderate to severe (4-10). Univariate and Multivariate logistic regression analyses were performed to determine predictive factors. Results: Text message with ESAS completion link was sent to 653 consecutive patients, of whom 509 (77.9%) completed the questionnaire during the study period. The median age was 52 years and 53.2 % were males. The primary cancer site was lung, breast, gastrointestinal, and others in 27.7%, 28.1%, 14.4%, and 29.8%, respectively. Pain (59.1%) and tiredness (57.8%) constituted the maximum physical symptom scores in moderate and severe category. Patients with lung cancer had most physical symptoms categorised in moderate and severe compared with other cancers (P = 0.03), while psychological symptoms were more likely to be moderate to severe females (P = 0.01). In the multivariable logistic regression analysis after adjusting for measured confounders, primary cancer site (lung vs others) and sex (female vs male) were significant predictors for moderate and severe physical and psychological symptoms, respectively. Conclusions: A simple mobile-based ESAS delivery system can help in collecting PRO data to assess symptom burden in patients diagnosed with cancer. The symptom burden assessment can aid in providing personalised symptom-directed measures to improve the quality of life of patients 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".