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
Abstract Malignant disease arising in the maxillary, ethmoid, frontal, or sphenoid sinuses, collectively known as the paranasal sinuses, is rare. Paranasal sinus cancer represents less than 5% of all head and neck malignancy, which in turn comprises less than 10% of malignancy overall. The majority of paranasal sinus cancers arise within the maxillary sinus (70–80%) followed by the ethmoid sinus (10–20%). Because of this, much of the literature (including the present chapter) focuses on data derived from description of tumors arising at these two sites. Cancers arising in the sphenoid or frontal sinuses are extremely rare. The outcome of patients presenting with paranasal sinus cancer is generally poor, with most centers reporting 5‐year survival rates in the range of 30–40%. As with any rare disease, the task of reliably identifying and validating independent prognostic factors for paranasal sinus cancer is complicated by the lack of prospectively collected data and the variability of data reported in the retrospective literature that spans many decades, with most series containing relatively small numbers of patients. Reports frequently describe patients with a wide range of tumor extent and histology treated with variable treatment approaches. Outcomes are often analyzed and reported with respect to different endpoints. Prognostication and empiric management recommendations are regularly based on conclusions drawn from the comparison of inhomogeneous treatment groups. In this chapter we attempt to identify prognostic factors that are supported by currently available data and, of equal importance, those that do not enjoy this support.
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.
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) | 0.001 | 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) | 0.152 | 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 it