Are there different phenotypes of thoracic surgery patients? A latent class analysis of pretreatment patient-reported outcomes
Bibliographic record
Abstract
Background: Among patients undergoing thoracic surgery, quality of life is associated with multiple perioperative outcomes. Whether patients suffer reduced quality of life in certain areas compared to others is unclear. Knowing this could direct risk mitigation interventions for patients who share common symptoms. The objective of this study was to determine whether patients can be subdivided into groups based on preoperative quality of life. Methods: statistic. Class separation was measured using normalized entropy statistics. Results: statistic and entropy showed increased preference for models as the number of classes decreased. Within the 3-class model, class 1 demonstrated a 73% to 100% probability of endorsing low impairment across all EQ-5D-3L dimensions, class 2 demonstrated a 93% probability of at least some impairment in mobility, and class 3 showed an 81% probability of moderate pain. Conclusions: There is evidence that patients undergoing thoracic surgery can be divided into 3 latent classes based on EQ-5D-3L score: low symptom burden, mobility-pain complex, and pain predominant. By identifying patients using these latent classes, targeted supportive interventions may be offered in the pretreatment period to improve perioperative outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".