Comparing chair time required for systemic agent drug reaction (DR) management to allocated chair time for systemic protocol.
Bibliographic record
Abstract
40 Background: Chemotherapy scheduling and determining associated nursing resources are complex multiobjective decision problems. Management of DRs are thought to take additional chair and nurse time and should be accounted for in appointment scheduling in order to having an efficiently running clinic. BC Cancer – Victoria is a tertiary care regional site of BC Cancer in Victoria, British Columbia, Canada and currently schedules patients using a regimen-based resource intensity model (Greene et al, 2012). Methods: DRs were tracked in real time over 3 months, where BC Cancer ID, reaction, protocol, agent, cycle number and time of reaction were recorded. Our electronic medical record was interrogated to determine start and end of chair time. We then compared the actual chair time required to manage patients with a DR to scheduled chair time. Results: Thirty reactions occurred, including DRs to paclitaxel (43%), rituximab (13%), docetaxel (10%), nivolumab (7%), oxaliplatin (7%), pertuzumab (7%), bendmustine (3%), and daratumumab (3%). The reactions most commonly occurred with cycles 1 or 2. The averages for total chair time during DR, time from arrival to reaction, and time from reaction to discharge were 281 min (+79.5), 106 min (+ 55), and 171 min (+ 94), respectively. An average of 449 min/month extra were required for DR management above scheduled chair time. Conclusions: The agents precipitating DRs were common and predictable. An average of 7 hours and 29 minutes per month above scheduled chair time was required to manage DRs. This information could be used to more efficiently schedule chair and nursing time. This also highlights the need to minimize the number of DRs as management is resource intensive.[Table: see text]
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".