A time and motion study of subcutaneous versus intravenous trastuzumab in patients with <scp>HER</scp>2‐positive early breast cancer
Bibliographic record
Abstract
Within PrefHer (NCT01401166), patients and healthcare professionals (HCPs) preferred subcutaneous (SC) over intravenous (IV) trastuzumab. We undertook a prospective, observational time and motion study to quantify patients' time in infusion chairs and active HCP time in PrefHer. Patients with HER2-positive early breast cancer received four adjuvant cycles of SC trastuzumab (600 mg fixed dose via SC single-use injection device [SID, Cohort 1] or SC handheld syringe [HHS, Cohort 2]) then four cycles of standard IV trastuzumab or the reverse sequence. Generic case report forms for IV and SC management, both in the treatment room and the drug preparation area, were tailored to reflect center practices. Patient chair time and active HCP time were recorded. We compared pooled Cohort 1 + 2 IV with Cohort 1 SC SID and Cohort 2 SC HHS mean times across eight countries and individually within them utilizing a random intercept generalized linear mixed-effects model. Per session, the SC SID saved a mean of 57 min of patient chair time versus IV (range across countries: 47-86; P < 0.0001); the SC HHS saved 55 min (40-81; P < 0.0001). Active HCP time was reduced by a mean of 13 min per session with the SC SID (range across countries: 4-16; P < 0.0001) and 17 min with the SC HHS (5-28; P < 0.0001) versus IV. SC trastuzumab, delivered via SID or HHS, saved patient chair and active HCP times versus IV infusion, supporting a transition to either SC method.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".