Differences between Cancer Patients’ Symptoms Reported by Themselves and in Medical Records
Bibliographic record
Abstract
Introduction: Data regarding rates of medical records concerning patients’ symptoms are controversial. We aimed to calculate medical discovery rate of patients’ symptoms and its association with symptoms severity. Methods: Patients reported symptoms were obtained by EORTC questionnaires of Quality of Life. Medical discovery rate was calculated after collected data on symptoms reported in medical records. Statistical descriptive methods were used. Results: There were 148 cancer patients. Most frequently reported symptoms were fatigue (80%), pain (66%), insomnia (64%). Symptoms with highest medical discovery rate were pain (19%) and nausea (14%). The remaining symptoms had low medical records discovery rate. More severe dyspnea, insomnia, nausea and constipation were more likely to be recorded by medical doctors (p<0.05). Conclusions: Majority of patients reported symptoms were not reported by doctor, even though symptoms could have been acknowledged and discussed with patients. Our results support the use of validated questionnaires to assess systematically patients’ symptoms.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".