133 Does “5+5” Equal Better Radiation Treatment Plans in Head and Neck Cancers?
Bibliographic record
Abstract
regression model using forward selection to identify factors independently predicting early death.Three-fold cross validation and bootstrapping using 500 random samples was performed.The PILLiR-M was created based on this model.Results: Two hundred and twelve patients who had received liver EBRT were identified.One hundred and nine patients were excluded (single fraction EBRT n=17, HCC n=63, cholangiocarcinoma n=22, benign pathology n=1, follow-up <4 months n=6).One hundred and three patients remained for analysis with a fourmonth mortality rate of 27.2%.Ascites, elevated bilirubin, low serum albumin, Child-Pugh score, ECOG performance status, non-colorectal primary, presence of extrahepatic disease and previous liver directed therapy were found to be significant predictors for early death on univariate analysis and were included in the multiple logistic regression model.Using forward selection to maximize the area under the curve, non-colorectal primary, ECOG, presence of extrahepatic disease and serum albumin were found to best predict patients unlikely to live longer than four months.A prognostic index (PILLiR-M) was created with 1 point for each of the following: non-colorectal primary, presence of extrahepatic disease, ECOG ≥2 and serum albumin<35g/L (AUC 0.852).Four month mortality was 0%, 3.1%, 43.6%, 66.7% and 100% for patients with 0 (n=16), 1 (n=32), 2 (n=39), 3 (n=9) and 4 (n=3) points, respectively.Conclusions: Non-colorectal primary, presence of extrahepatic disease, poor performance status and decreased serum albumin were found to be risk factors for mortality less than four months in patients being considered for liver directed EBRT for metastatic disease.We successfully created the PILLiR-M to aid clinicians in determining which patients are unlikely to live long enough to benefit from liver EBRT.Future work includes validating the PILLiR-M with an independent dataset.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".