A New Method of Odds Ratio and Hazard Analysis of Head and Neck Cancer
Bibliographic record
Abstract
The main topic of this paper is to focus on a new method for calculating odds ratios and hazard ratios through probabilities and effect modification. This probability is derived through an odds ratio proof for the common conditional odds ratio of Cochran Mantel Hansel showing theta equals one. Subsequently, the probability formula is obtained and the hazard ratio expression derived. However, the new relation of this proof is to show that logits equals itself through probability. From this derivation, an expression of risk is obtained which is an odds ratio. Parameters are obtained through a novel method of Survreg and its proportional hazard assumption. The odds ratio obtained is given as per strata as well as hazard ratio method demonstrated which is curvilinear to probability in comparison for the interaction model to represent percent change. The odds ratios from PROC GLIMMIX for interaction model has odds ratio of 1.76 vs 1.73 and 1.83 vs 1.76 for white and black males of a logit expression another expression of a logit. A parametric analysis shows correlation to the odds ratios for strata and probability Pr(z) that can work from a new derivation for an odds ratio with for the exposure shown to have power with the RANTBL function of about 83 % with effect modification included at 100% power. The comparison of effect modification P values to hazard ratio is then made for differences across strata.
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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".