Feasibility, acceptability, and efficacy of a proactive telephone intervention to improve toxicity management during chemotherapy.
Bibliographic record
Abstract
105 Background: Chemotherapy (chemo) is associated with a significant risk of toxicity, which often peaks between ambulatory visits. Consequently, effective remote symptom management support is essential to optimize self-management and resource use, including emergency department visits and hospitalizations (ED+H) during chemo. The aim of this study was to examine the feasibility, acceptability and effects of a telephone management intervention on symptomatic toxicity and resource use during chemo for early stage breast cancer (EBC). Methods: A prospective study of telephone-based toxicity management among women receiving neo-adjuvant or adjuvant chemo for EBC was undertaken at one urban and one rural site in Ontario, Canada. The intervention consisted of two standardized calls by nurses assessing common toxicities after each chemo (call 1 within 3 days and call 2 within 8-10 days). Primary outcome measures were feasibility and acceptability based on patient (pt) and clinician feedback. Efficacy was evaluated by self-reported ED+H. Results: Between 09/2013 and 12/2014, 77 women with EBC were enrolled (mean age 55 years). Most commonly used regimens were AC-paclitaxel (58%) and FEC-docetaxel (16%). 78% of pts received primary GCSF prophylaxis. Adherence with calls was 82%; mean call duration was 9 minutes. The intervention was well received by both pts and clinicians. 97% of pts indicated they liked receiving the calls and 94% would recommend this protocol be offered to all pts receiving chemo. Clinicians and pts felt the calls reduced pt anxiety by providing just-in-time education and counselling. Twenty five (33%) pts reported at least one ED+H during chemo, lower than the historical rate of 44% for this population in Ontario. Challenges included introducing an intervention that involved both routine clinical personnel and research staff and incorporating the calls into existing work responsibilities. Conclusions: Telephone-based toxicity management during ESB chemo is feasible, perceived as valuable by clinicians and pts, and may be associated with lower rates of acute care use. Larger scale evaluations of this approach focusing on effectiveness are warranted.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".