Prediction Tools for Unfavourable Outcomes in Clostridium difficile Infection: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Identifying patients at risk for adverse outcomes of Clostridium difficile infection (CDI), including recurrence and death, will become increasingly important as novel therapies emerge, which are more effective than traditional approaches but very expensive. Clinical prediction rules (CPRs) can improve the accuracy of medical decision-making. Several CPRs have been developed for CDI, but none has gained a widespread acceptance. METHODS: We systematically reviewed studies describing the derivation or validation of CPRs for unfavourable outcomes of CDI, in medical databases (Medline, Embase, PubMed, Web of Science and Cochrane) and abstracts of conferences. RESULTS: Of 2945 titles and abstracts screened, 13 studies on the derivation of a CPR were identified: two on recurrences, five on complications (including mortality), five on mortality alone and one on response to treatment. Two studies on the validation of different severity indices were also retrieved. Most CPRs were developed as secondary analyses using cohorts assembled for other purposes. CPRs presented several methodological limitations that could explain their limited use in clinical practice. Except for leukocytosis, albumin and age, there was much heterogeneity in the variables used, and most studies were limited by small sample sizes. Eight models used a retrospective design. Only four studies reported the incidence of the outcome of interest, even if this is essential to evaluate the potential usefulness of a model in other populations. Only five studies performed multivariate analyses to adjust for confounders. CONCLUSIONS: The lack of weighing variables, of validation, calibration and measures of reproducibility, the weak validities and performances when assessed, and the absence of sensitivity analyses, all led to suboptimal quality and debatable utility of those CPRs. Evidence-based tools developed through appropriate prospective cohorts would be more valuable for clinicians than empirically-developed CPRs.
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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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".