A preliminary study of attitudes toward the assessment and management of cancer cachexia among medical oncologists and nurses.
Bibliographic record
Abstract
44 Background: A recent international consensus on the definition and classification of the cancer anorexia / cachexia syndrome (CACS) will facilitate clinical trial design, development of practice guidelines, and routine clinical management. Non-pharmacological interventions such as dietary counseling and promising new drugs have demonstrated improved outcomes in preliminary trials. Management of nutritional impact symptoms such as severe pain, depression, early satiety and chronic nausea also produce weight gain. These important advances contrast with the apparent low priority given to this condition by oncological societies worldwide. Our objective was to evaluate the attitudes of medical oncologists and nurses in the assessment and management of CACS in non-small cell lung cancer. Methods: Surveys were administered electronically to US-based, community medical oncologists (n=76 respondents) and oncology nurses (n=25), members of the Sermo research database which includes over 275,000 active healthcare providers, pre-qualified through telephone or online screening. A proprietary MedPulse tool achieved random geographic distribution of respondents through a staged query–response process. Results: 67% of medical oncologists selected weight loss as the most important criterion for diagnosing CACS in their daily practice, consistent with the consensus definition. However, only 4% of respondents described CACS as inevitable or very likely to develop in patients maintaining good performance status through first-line therapy. Community providers identified the management of symptoms that affect appetite as very important (58.8%), important (31.4%) or somewhat important (7.8%), but only 9.8% indicated they currently use a formal tool to evaluate these symptoms. 72% would consider using a brief assessment tool in clinical practice. Conclusions: Our surveys suggest community oncologists recognize the core criteria for the diagnosis of CACS, although there may be under-recognition of the condition’s prevalence. There is considerable interest in adopting a brief symptom assessment tool for screening, management and referral of affected or at-risk patients.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".