P2-315 The effectiveness of systematic symptom assessment to improve patient care in oncology medicine: a systematic review
Bibliographic record
Abstract
Background Cancer patients do not voluntarily reveal all symptoms they experience. By asking them about symptoms using a structured systematic method, clinicians can find out about other symptoms. However, does systematic symptom assessment result in improved symptom control? Objectives To determine and identify the potential association between systematic symptom assessment and a cancer patient9s well-being and to gather information for future research. Methods Electronic bibliographic databases were searched to July 2009. Search sources included MEDLINE, EMBASE, HealthSTAR, CINAHL, Cochrane Database of Systematic Reviews, Health and Psychosocial Instruments and Proquest Dissertations and Theses. Criteria for inclusion were: published full papers in English reporting controlled clinical trials or systematic reviews examining the effects of systematic symptom assessment on cancer patients9 well-being. Results 54 articles required full paper review by two reviewers, from which another 13 papers were reviewed arising from the reference lists. Five studies conducted from 1991 to 2006 met the eligibility criteria and were included in the analysis. The studies found that in the implementation of systematic symptom assessment in oncology care provided improved care in terms of pain management, overall symptom distress, quality of life, and aspects of patient and physician communication. Conclusions Given limitations presented in the five studies, it is impossible to draw firm conclusions. However, this review is able to conclude that systematic symptom assessment provides valuable information in the overall assessment of the patient and its feedback to the clinicians do project an overall improvement in the patient9s physical and mental well-being.
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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 | 0.029 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".