Remote, proactive, telephone based management of toxicity in outpatients during adjuvant or neoadjuvant chemotherapy for early stage breast cancer: pragmatic, cluster randomised trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of remote proactive management of toxicities during chemotherapy for early stage breast cancer. DESIGN: Pragmatic, cluster randomised trial. SETTING: 20 cancer centres in Ontario, Canada, allocated by covariate constrained randomisation to remote management of toxicities or routine care. PARTICIPANTS: All patients starting adjuvant or neoadjuvant chemotherapy for early stage breast cancer at each centre. 25 patients from each centre completed patient reported outcome questionnaires. INTERVENTIONS: Proactive, standardised, nurse led telephone management of common toxicities at two time points after each chemotherapy cycle. MAIN OUTCOME MEASURES: The primary outcome, cluster level mean number of visits to the emergency department or admissions to hospital per patient during the whole course of chemotherapy treatment, was evaluated with routinely available administrative healthcare data. Secondary patient reported outcomes included toxicity, self-efficacy, and quality of life. RESULTS: Baseline characteristics of participants were similar in the intervention (n=944) and control arms (n=1214); 22% were older than 65 years. Penetration (that is, the percentage of patients who received the intervention at each centre) was 50-86%. Mean number of visits to the emergency department or admissions to hospital per patient was 0.91 (standard deviation 0.28) in the intervention arm and 0.94 (0.40) in the control arm (P=0.94); 47% (1014 of 2158 patients) had at least one visit to the emergency department or a hospital admission during chemotherapy. Among 580 participants who completed the patient reported outcome questionnaires, at least one grade 3 toxicity was reported by 48% (134 of 278 patients) in the intervention arm and by 58% (163 of 283) in the control arm. No differences in self-efficacy, anxiety, or depression were found. Compared with baseline, the functional assessment of cancer therapy trial outcome index decreased by 6.1 and 9.0 points in the intervention and control participants, respectively. CONCLUSIONS: Proactive, telephone based management of toxicities during chemotherapy did not result in fewer visits to the emergency department or hospital admissions. With the rapid rise in remote care because of the covid-19 pandemic, identifying scalable strategies for remote management of patients during cancer treatment is particularly relevant. TRIAL REGISTRATION: ClinicalTrials.gov NCT02485678.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".