Fidaxomicin to prevent recurrent <i>Clostridioides difficile</i>: what will it cost in the USA and Canada?
Bibliographic record
Abstract
Importance: infections (CDI) have placed fidaxomicin as a first-line treatment. Objective: To estimate the net cost of first-line fidaxomicin compared to vancomycin in the American and Canadian healthcare systems and to estimate the price points at which fidaxomicin would become cost saving for the prevention of recurrence. Data sources and study selection: We identified randomized, placebo-controlled trials directly comparing fidaxomicin with vancomycin that reported on recurrence. Medication costs were obtained from the Veterans Affairs Federal Supply Schedule (US) and the Quebec drug formulary (Canada). The average cost of a CDI recurrence was established through a systematic review for each country. Data extraction synthesis and outcome measures: For efficacy, data on CDI recurrence at day 40 were pooled using a restricted maximal likelihood random effects model. For the cost review, the mean cost across identified studies was adjusted to reflect May 2022 dollars. These were used to estimate the net cost per recurrence prevented with fidaxomicin and the price point below which fidaxomicin would be cost saving. Results: The estimated mean system costs of a CDI recurrence were $15 147USD and $8806CAD, respectively. Preventing one recurrence by using first-line fidaxomicin over vancomycin would cost $38 222USD (95%CI $30 577-$57 332) and $13 760CAD (95%CI $11 008-$20 640), respectively. The probability that fidaxomicin was cost saving exceeded 95% if priced below $1140USD or $860CAD, respectively. Conclusions and Relevance: An increased drug expenditure on fidaxomicin may not be offset through recurrence prevention unless the fidaxomicin price is negotiated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".