WHY STOP? Quantifying Cognitive-Behavioural factors that influence the impact of PCR-POCT results on antibiotic cessation in ICU
Bibliographic record
Abstract
Abstract INTRODUCTION: Rapid Point of Care Tests for infection (POCT) do not consistently improve antibiotic stewardship (ASP) of suspected ICU infection. We measured 1) the effect of a negative PCR-POCT on antibiotic stop decisions, and 2) clinico-behavioural factors that prevent stopping.METHODS: Vignettes of antibiotic treated respiratory infection, with 4 distinct trajectories were presented to ICU clinicians: overall improvement, clinical improvement/biological worsening, clinical worsening/biological improvement, overall worsening. Initial and post PCR-POCT antibiotic decisions (stop or continue) /confidence levels were recorded. The PCR-POCT offer was voluntary but always presented and negative. Linear regression determined association of their final decision with influencing factors.RESULTS: Seventy clinicians responded. A negative PCR-POCT increased stop decisions in all scenarios (p<0.001) except improvement (already high); especially in discordant clin worse(49% pre-POCT vs 74% post-POCT). Inclination to stop was reduced by an ambiguous/worsening trajectory(p=0.015), initial confidence to continue(p<0.001), and involuntary receipt of POCT(p<0.001), not clinician experience or risk averseness. CONCLUSIONS: Negative PCR-POCT increases the inclination to stop antibiotics, particularly in ambiguous/worsening trajectories of ICU infection. Clinician intuition to continue and disinterest in POCT reduce its influence to stop. Highlighting and quantifying the predictive impact of behavioural-trajectorial factors can improve antibiotic stewardship and study design in ICU related infection.
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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 | 0.046 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".