The Oncology Symptom Control and Information Resource (OSCIR): Using information technology to improve the management of cancer treatment-related side effects
Bibliographic record
Abstract
17031 Background: Management of treatment related side effects remains a challenge despite availability of effective therapies and management guidelines. The main reasons include ineffective assessment of the patient's experience, inability to access prevention and management guidelines in a timely manner, poor documentation and inefficient communication within the patient's care team. We postulate treatment related side effects could be more effectively managed using software designed to assist assessment and improve communication of the patient's experience. Methods: Software specifications included internet-based wireless capability, linking to existing electronic databases and facilitated data entry (drop down menus, check boxes, pop up windows, etc.). Oncology literature was reviewed to identify appropriate symptom assessment questionnaires, prevention and treatment strategies. The National Cancer Institute Common Terminology Criteria for Adverse Events v.3.0 was adapted. An iterative process was used for software development; content and functionality was discussed with programmers on an ongoing basis. This process is aimed at providing software that more completely meets the needs of users. Feedback from oncology pharmacists was sought regularly. Results: OSCIR has been developed to facilitate the assessment and management of nausea, vomiting, diarrhea, constipation, mucositis, and palmar-plantar erythrodysesthesia. Each side effect module includes onset and resolution, signs and symptoms, automated grading, non- and pharmacological management. Program features include downloading of electronic demographic information, chemotherapy regimen, laboratory results, call back roster, and communication tools. Conclusions: Using the iterative process we have created a program that addresses the needs of pharmacists involved in the assessment and management of treatment-related side effects. Further development will incorporate feedback from other members of the patient's care team and will include additional side effects and features. No significant financial relationships to disclose.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".