Dispositional Optimism Predicts Survival Status 1 Year After Diagnosis in Head and Neck Cancer Patients
Bibliographic record
Abstract
PURPOSE: The aim of this study was to investigate the hypothesis that, independent of other known prognostic factors, pessimistic head and neck (H&N) cancer patients have a greater risk of being dead 1 year after diagnosis than do optimistic patients. PATIENTS AND METHODS: A prospective observational study design was used with a cohort of H&N cancer patients diagnosed during the period from March 1, 1997, to August 31, 1998, at the Centre Hospitalier Universitaire, Clermont-Ferrand, France. Dispositional optimism (DO) was evaluated at baseline using a French version of the Life Orientation Test translated and validated for this study. One-year survival status was collected on all subjects. The analysis of the hypothesized association between DO and 1-year survival was performed using multiple logistic regression analysis, controlling for other sociodemographic and clinical variables. RESULTS: The sample size was 101 patients, representing all but one of those patients fitting the inclusion criteria who were diagnosed during the recruitment period. Of these, 51 were alive at 1 year after diagnosis, 45 were dead, and five were lost to follow-up. The multivariate analysis was performed on the data from the 96 subjects in whom 1-year survival status was known. Controlling for known predictors of H&N cancer survival, pessimistic subjects (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.01 to 1.24) and those living alone (OR, 4.14; 95% CI, 1.21 to 14.17) were more likely than optimistic subjects and those living with others to be dead at 1 year. CONCLUSION: The results of this study of a cohort of French H&N cancer patients indicate that dispositional optimism predicts 1-year survival independent of other sociodemographic and clinical variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| 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".