Bibliographic record
Abstract
Canada’s Drug Agency (CDA-AMC) recommends that Keytruda should be reimbursed by public drug plans for the treatment of adult patients with resectable locally advanced head and neck squamous cell carcinoma (HNSCC) whose tumours express PD-L1 (combined positive score [CPS] ≥ 1), as determined by a validated test, as neoadjuvant treatment (before surgery) as monotherapy, continued as adjuvant treatment (after surgery) in combination with radiotherapy (RT) with or without cisplatin, and then as monotherapy, if certain conditions are met. Keytruda should only be covered to treat patients aged 18 years or older who have newly diagnosed, locally advanced HNSCC that can be removed by surgery. Their tumours must test positive for PD-L1 (CPS ≥ 1), and they should be in relatively good health (as measured by performance status). Keytruda should not be covered for patients who have more advanced disease (T4b and/or N3 cancer stage or distant spread); cancer outside the mouth, throat, or voice box; or have received prior treatment for head and neck cancer. Exceptions may be made for patients with resectable recurrence more than 6 months after previous treatment. Keytruda should only be reimbursed if it is prescribed by clinicians with expertise in managing head and neck cancer and the cost of Keytruda is reduced.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".