Datasets:
metadata
license: mit
language:
- en
pretty_name: ABX-PD-009 Inoculum Effect Coherence
tags:
- antibiotics
- pharmacodynamics
- resistance
- tabular
task_categories:
- tabular-classification
size_categories:
- n<1k
ABX-PD-009 Inoculum Effect Coherence
Purpose
Detect disproportionate efficacy loss at higher bacterial loads.
The key pattern
- inoculum rises
- exposure stays stable
- MIC stays stable
- killing drops too much
Files
- data/train.csv
- data/test.csv
- scorer.py
Schema
Each row is one inoculum condition in an ordered series.
Required columns
- row_id
- series_id
- inoculum_rank
- organism
- strain_id
- antibiotic_name
- antibiotic_class
- exposure_index
- mic_mg_L
- cfu0_log10
- cfu24_log10
- kill_24_log10
- media
- assay_method
- source_type
- inoculum_effect_signal
- earliest_inoculum_effect
Labels
inoculum_effect_signal
- 1 for rows at or after the first detected disproportionate loss
earliest_inoculum_effect
- 1 only for the first detected row in that series
Evaluation
Run
- python scorer.py --path data/test.csv
Scorer logic in v1
- baseline is inoculum_rank 1
- event triggers when
- cfu0 rises by 1.0 log10 or more
- kill drops by 0.8 log10 or more vs baseline
- exposure_index stays within 10 percent of baseline
- MIC stays within 2 fold of baseline