Unplanned visits to hospital emergency for oropharynx patients undergoing radiation therapy in Ontario.
Bibliographic record
Abstract
267 Background: Radiation therapy (RT) with or without concurrent chemotherapy is the standard of care for patients with oropharyngeal cancer. The acute toxicity of treatment is well recognized and highlights the need for a multidisciplinary approach to management. In 2009, a Head and Neck Organizational Guideline was published by Cancer Care Ontario (CCO) which emphasizes the need for a multidisciplinary team approach of surgical, medical and radiation oncologists; supportive care professionals, including speech language pathologists, dieticians, social workers; and others to support the care of this population. In order to assess the availability of appropriate supportive care for this patient group, the percentage of patients who visited an Emergency Department (ED) during their course of RT and the causes for these visits were assessed. Methods: Patients with a diagnosis of oropharyngeal cancer receiving radical RT in Ontario (10 Cancer Centres) between the period of April 1, 2011 and March 31, 2013 were identified from CCO’s Activity Level Reporting ( ALR ) database. These patients were then linked to the National Ambulatory Care Reporting System ( NACRS ) administrative dataset from the Canadian Institute for Health Information ( CIHI ) which allowed identification of patients that visited ED during their course of RT as well the reasons for the visits. Data on the use of concurrent chemotherapy was not available. Results: Over the two year period, 885 patients in Ontario had radical RT (+ concurrent chemotherapy) for oropharyngeal cancer of which 261 (29.5%) visited an ED at least once during their treatment (range of 22% to 36% across the 10 centres). The main reasons for the ED visits were dehydration and neutropenia, although coding for reason of visit was incomplete in 16% of cases. Conclusions: The high proportion of patients attending an ED while receiving daily RT would suggest that the supportive care needs of this population are not being regularly met. Further work is necessary to understand the reasons for ED visits in this group of patients and the substantial variation seen between centres would suggest this metric may be useful as a new quality of care indicator.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".