---
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