Cancer cachexia in thoracic malignancy: a narrative review
Bibliographic record
Abstract
PURPOSE OF REVIEW: Thoracic malignancies are amongst the most lethal of all cancers. Cancer cachexia lacks unanimously accepted diagnostic criteria, and therefore is referenced to as a conceptual framework whereby cancer cachexia is 'an ongoing loss of skeletal muscle mass (termed sarcopenia), with or without loss of fat mass that cannot be reversed by conventional nutritional support and leads to progressive functional impairment'. This review summarises the current evidence base in this field, including imaging techniques currently used to define sarcopenia, inflammatory and metabolic changes associated with the syndrome and ongoing research into potential treatment strategies. RECENT FINDINGS: Sarcopenia is a key component of the cancer cachexia syndrome. It is common in patients with both early-stage and advanced NSCLC. Patients with sarcopenia have more treatment-related side effects and poorer overall survival compared with nonsarcopenic patients. SUMMARY: Early identification of cancer cachexia may facilitate stratification of patients most-at-risk and initiation of emerging anticachexia treatments. If these are proven to be effective, this strategy has the potential to improve tolerance to anti-cancer therapies, improving the quality of life, and perhaps the survival, of patients with thoracic malignancies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".