Outcome of Curative Management of Malignant Tumours of the Parotid Gland
Bibliographic record
Abstract
PURPOSE: The optimal management of malignant parotid gland tumours remains to be defined precisely. Specifically, a further understanding of the tumour features that influence treatment outcome is needed. MATERIALS AND METHODS: A retrospective review was conducted on 184 patients who were registered at the Princess Margaret Hospital with a diagnosis of a primary malignant parotid gland tumour. RESULTS: All patients were initially managed with a parotidectomy, and postoperative x-ray radiation therapy (XRT) was administered to 159 patients. The actuarial 5-year cause-specific survival and locoregional control rates were 76% and 81%, respectively. The survival and locoregional control rates for patients treated with surgery alone versus surgery plus postoperative XRT were not statistically different. A multiple regression analysis identified only age and tumour category to be independently significant prognostic factors for both survival and locoregional control. CONCLUSION: We would recommend that patients with malignant parotid gland tumours be managed with parotidectomy, followed by postoperative XRT for tumours with residual disease, aggressive histology, and/or positive lymph nodes.
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 | 0.003 |
| 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) | 0.001 | 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 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".