Distinct Morning and Evening Fatigue Profiles in Patients With Gynecologic Cancers Receiving Chemotherapy
Bibliographic record
Abstract
OBJECTIVES: To identify distinct morning and evening fatigue profiles in patients with gynecologic cancers and evaluate for differences in demographic and clinical characteristics, common symptoms, and quality-of-life outcomes. SAMPLE & SETTING: Outpatients with gynecologic cancers (N = 233) were recruited before their second or third cycles of chemotherapy at four cancer centers in San Francisco Bay and New York. METHODS & VARIABLES: The Lee Fatigue Scale was completed six times over two cycles of chemotherapy in the morning and in the evening. Latent profile analysis was used to identify distinct morning and evening fatigue profiles. RESULTS: Four distinct morning and two distinct evening fatigue classes were identified. Common risk factors for morning and evening fatigue included younger age, higher body mass index, lower functional status, and higher comorbidity burden. Patients in the worst morning and evening fatigue classes reported higher levels of anxiety, depression, and sleep disturbance; lower levels of energy and cognitive function; and poorer quality of life. IMPLICATIONS FOR NURSING: Clinicians can use this information to identify higher-risk patients and develop individualized interventions for morning and evening fatigue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".