Bibliographic record
Abstract
To theEditor—We thank Dr Ito for thoughtful observations about our research and we appreciate the opportunity to respond [1]. In a matched retrospective observational cohort of 1842 patients, we found that patients receiving outpatient parenteral antimicrobial therapy (OPAT) had a similar number of adverse events and lower direct healthcare costs than patients receiving inpatient parenteral antimicrobial therapy (IPAT) [2]. Dr Ito highlights that unobserved differences in treatment adherence might have influenced our findings [1]. We believe that this raises two related but distinct issues. First, hospitalized patients receiving IPAT have intravenous antimicrobials directly administered by nurses. In contrast, community-based patients receiving OPAT plausibly face more logistical barriers (eg, missed appointments and homecare visits, undelivered drugs, or venous access malfunction) that result in delayed, incomplete, or omitted antimicrobial doses. In a randomized trial, these factors might reduce antimicrobial adherence and produce less favorable outcomes among these patients. Second, physicians in clinical practice do not randomly allocate patients to OPAT or IPAT. Real-world outcomes might appear better with OPAT because physicians selecting patients for outpatient care probably “cherry-pick” the healthiest and most treatment-adherent patients who already have a better-than-average prognosis [3]. We used matching and adjustment to account for prognostically important baseline differences between OPAT and IPAT patient groups in our study, but our adjusted effect estimates are still subject to bias from residual confounding [2]. This remains a previously acknowledged limitation of our study. Our results nonetheless suggest that OPAT is a safe and effective choice for appropriately selected patients. The substantial cost savings arising from the transition from IPAT to OPAT should be reallocated to support adherence and expand delivery of OPAT in the community [4]. Author contributions. All authors were responsible for drafting and revising this reply. Disclaimer. Funding organizations were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review and approval of this manuscript. Financial support. The original study was supported by an unrestricted grant from the British Columbia Infectious Diseases Society. J. A. S. was supported by the Vancouver Coastal Health Research Institute and by a health professional–investigator award from Michael Smith Health Research 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 | 0.005 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.019 | 0.037 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".