Why newly diagnosed cancer patients require supportive care? An audit from a regional cancer center in India
Bibliographic record
Abstract
PURPOSE: The present study was planned to record the distressing symptoms of newly diagnosed cancer patients and evaluate how the symptoms were addressed by the treating oncologists. MATERIALS AND METHODS: All newly diagnosed cancer patients referred to the Department of Radiotherapy during May 2014 were asked to complete a questionnaire after taking their consent. The Edmonton symptom assessment scale-regular questionnaire was used to assess the frequency and intensity of distressing symptoms. The case records of these patients were then reviewed to compare the frequency and intensity documented by the treating physician. The difference in the two sets of symptoms documented was statistically analyzed by nonparametric tests using SPSS software version 16. RESULTS: Eighty-nine patients participated in this study, of which only 19 could fill the questionnaire on their own. Anxiety was the most common symptom (97.8%) followed by depression (89.9%), tiredness (89.9%), and pain (86.5%). The treating physicians recorded pain in 83.1% whereas the other symptoms were either not documented or grossly underreported. Anxiety was documented in 3/87 patients, but depression was not documented in any. Tiredness was documented in 12/80 patients, and loss of appetite in 54/77 patients mentioning them in the questionnaire. Significant statistical correlation could be seen between the presence of pain, anxiety, depression, tiredness, and loss of appetite in the patients. CONCLUSION: The study reveals that the distressing symptoms experienced by newly diagnosed cancer patients are grossly underreported and inadequately addressed by treating oncologists. Sensitizing the oncologists and incorporating palliative care principles early in the management of cancer patients could improve their holistic care.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".