Patterns of care for the initial management of cervical cancer in Ontario.
Bibliographic record
Abstract
OBJECTIVE: To facilitate the planning of resources for cancer services in Ontario, Cancer Care Ontario commissioned an evaluation of operative services delivered for cervical cancer. METHODS: Women with an incident diagnosis of cervical cancer were identified from 1 April, 2003 to 31 March, 2004 using the Ontario Cancer Registry. Record linkages were created to other provincial health databases such as the Ontario Health Insurance Plan. RESULTS: There were 513 incident cases. Disease-specific rates of cancer were higher in rural areas and those from lower income quintiles. Forty-three percent of women had no surgery. Use of surgery did not appear to vary by SEC, urban/rural residence or LHIN. Women of younger age were more like to receive surgery for cervical cancer. Gynecologists conducted 63% of the operations. Gynecologics were most likely to complete a lymphadenectomy (70.3%). All women were assessed by CXR. Only 22% of women had a CT scan of the abdomen and pelvis. Radiation consults were performed in half of the women with cervix cancer but treatment was only delivered to half of those seen. Medical oncologists saw about 10% of women with cervical cancers. CONCLUSIONS: There appear to be variations in incidence rates of cervical cancer, with cancers being more frequent in rural areas. In two-thirds of the population, surgery is performed in the region where the patient lives. Subspecialty care from gynecologic oncologists was provided to one-third of women. These preliminary data would be enhanced with further information such as comorbidity, treatment intent (palliative/curative), histology, grade and stage.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".