Symptoms in patients with lung carcinoma
Bibliographic record
Abstract
BACKGROUND: The patient perspective on distress associated with lung carcinoma is important, yet understudied. Previous research on symptom experience generally had not differentiated the dimension symptom intensity/frequency from which symptoms are associated with most distress. The objective of the current study was to determine whether patterns of symptom intensity were similar to patterns of symptom distress, whether patterns were consistent at different time points, whether patterns varied by subgroups, and whether high symptom intensity was equivalent to distress. METHODS: Four hundred adults who were newly diagnosed with inoperable lung carcinoma completed a measure of symptom intensity/frequency and a new measure of distress associated with symptoms at six time points during the first year after diagnosis. These data were supplemented by field notes by research nurses and by less structured, qualitative interviews. RESULTS: The mean ranking of distress in the total group and in all subgroups remained constant at all time points, with breathing, pain, and fatigue associated with the most distress. In contrast, the pattern of mean rank order of symptom intensity showed little consistency; however, fatigue had the highest intensity scores at all time points. CONCLUSIONS: The current data challenged the uncritical use of summated scores of different symptom items in the context of lung carcinoma. Breathing and pain appeared to function as icons representing threats associated with lung carcinoma, with distress described as related to the past and the present and to expectations for the future. One of the most promising implications of these data was in fostering a preventive paradigm for symptom palliation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".