Symptom clusters in patients with lung cancer: a literature review
Bibliographic record
Abstract
OBJECTIVE: To provide a review of literature reporting empirically determined symptom clusters in lung cancer patients. METHOD: We conducted a literature search on symptom clusters in lung cancer patients using MEDLINE, EMBASE and CINAHL. Studies examining the presence of predetermined clusters were excluded. The five relevant studies identified were published between 1997 and 2009. RESULTS: Overall, the five studies reported significantly diverse findings with regards to symptom cluster quantity and composition in lung cancer patients. The number of symptom clusters extracted varied from one to four per study. The number of symptoms in a cluster ranged from two to 11. The only cluster that was consistently identified in two studies was composed of nausea and vomiting symptoms. Respiratory clusters identified in two studies were also comparable, containing both dyspnea and cough, among other symptoms. Methodological disparities, including differences in sample population characteristics, assessment tools and analytical methods, were evident in the five studies reviewed. CONCLUSION: Symptom cluster exploration is a developing area of research in the oncology field and is promising in providing insights into diagnosis, prognostication and symptom management. Disparities in methodology are significant barriers to producing comparable results. These inconsistencies result in a lack of consensus in symptom clusters in lung cancer populations, thus impeding the determination of clinically relevant findings.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".