Novel Cellular and Immunotherapy: Toxicities and Perioperative Implications
Bibliographic record
Abstract
Targeted cellular and immunotherapies have welcomed a new chapter in multi-modal cancer therapy. These agents harness our innate immune system and destroy malignant cells in a precise way as compared with "legacy" chemotherapeutic agents that largely rely on abolishing cell division. New therapies can augment the T-cell recognition of tumor antigens and effectively prevent tumor cells from their historically successful ability to evade immune recognition. These novel agents cause acute and chronic toxicities to a variety of organ systems (enteritis, pneumonitis, hypophysitis, and hepatitis), and this may masquerade as other chronic illnesses or paraneoplastic effects. As the perioperative footprint of cancer patients increases, it is essential that perioperative providers-anesthesiologists, surgeons, nurse anesthetists, and inpatient hospital medicine providers-be up to date on the physiologic mechanisms that underlie these new therapies as well as their acute and subacute toxicity profiles. Immunotherapy toxicity can significantly impact perioperative morbidity as well as influence perioperative management, such as prophylaxis for adrenal insufficiency, preoperative pulmonary assessment, and screening for thyroid dysfunction, among others.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".