Adopting and Introducing New Technology To Improve Patient Care: A Wedding of Clinicians and Informatics Specialists
Bibliographic record
Abstract
The BC Cancer Agency sees 128,172 patients per year, of which 2,186 are referred to the Patient Symptom Management/Palliative Care (PSMPC) clinics for tertiary symptom management. Other than at the PSMPC clinics, screening for symptom distress is extremely variable because there is no systematic assessment protocol. In a recent audit of patients coming to the Cancer Agency, approximately 64% of patients reported experiencing a moderate to severe level of symptom distress. Of the total patients in the audit (n = 1,147), only 18 were seen by the PSMPC teams and it is unclear whether or not the remaining patients had their symptoms attended to by a health professional at the BCCA.The tool which the BCCA has chosen for screening and assessment is the Edmonton Symptom Assessment System (ESAS), which was developed by Dr. Eduardo Bruera. ESAS is a nine-item, self-reporting, visual analogue instrument used to measure pain and other symptoms using numeric ratings. Cancer Care Ontario (CCO) has developed an electronic means whereby patients' ESAS scores are entered and housed in an electronic health record and then used for triage. BCCA is in partnership with CCO to adapt this system for use in BC.
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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 | 0.039 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".