An automatic pipeline for temporal monitoring of radiotherapy-induced toxicities in head and neck cancer patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Radiotherapy for head and neck cancer often causes a spectrum of toxicities. Such toxicities are usually unavailable as structured data and are reported within textual clinical reports. To reduce the burden of manual assessment of toxicities, we propose a language processing model for the automatic extraction of toxicities. The cohort consists of 384 patients with head and neck cancer who underwent radiotherapy, either as monotherapy or in combination with chemotherapy or surgery. A total of 3510 notes were extracted. The toxicities were then manually annotated. Two tasks of toxicity mention detection and toxicity extraction were defined. Pre-trained language models such as BERT, Clinical BioBERT, and Clinical Longformer were fine-tuned. Our best model achieves an F1 score of 90% for automatic extraction of toxicity mentions. An automatic system enables real-time extraction of toxicities and insights into their temporal patterns, offering actionable data to support dose optimization and minimize toxicities in personalized treatments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it