Datasets:
row_id string | series_id string | inoculum_rank int64 | organism string | strain_id string | antibiotic_name string | antibiotic_class string | exposure_index float64 | mic_mg_L float64 | cfu0_log10 float64 | cfu24_log10 float64 | kill_24_log10 float64 | media string | assay_method string | source_type string | inoculum_effect_signal int64 | earliest_inoculum_effect int64 | notes string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ABXPD009-TR-0001 | S1 | 1 | Escherichia coli | EC-ATCC25922 | meropenem | carbapenem | 1 | 0.03 | 6 | 3 | 3 | CAMHB | inoculum_time_kill | simulated | 0 | 0 | baseline inoculum |
ABXPD009-TR-0002 | S1 | 2 | Escherichia coli | EC-ATCC25922 | meropenem | carbapenem | 1 | 0.03 | 7 | 4.4 | 2.6 | CAMHB | inoculum_time_kill | simulated | 0 | 0 | small loss expected |
ABXPD009-TR-0003 | S1 | 3 | Escherichia coli | EC-ATCC25922 | meropenem | carbapenem | 1 | 0.03 | 8 | 6 | 2 | CAMHB | inoculum_time_kill | simulated | 0 | 0 | still proportional |
ABXPD009-TR-0004 | S2 | 1 | Klebsiella pneumoniae | KP-CLIN250 | ceftriaxone | beta_lactam | 1 | 0.5 | 6.2 | 4 | 2.2 | CAMHB | inoculum_time_kill | simulated | 0 | 0 | baseline inoculum |
ABXPD009-TR-0005 | S2 | 2 | Klebsiella pneumoniae | KP-CLIN250 | ceftriaxone | beta_lactam | 1 | 0.5 | 7.2 | 6 | 1.2 | CAMHB | inoculum_time_kill | simulated | 1 | 1 | disproportionate loss begins |
ABXPD009-TR-0006 | S2 | 3 | Klebsiella pneumoniae | KP-CLIN250 | ceftriaxone | beta_lactam | 1 | 0.5 | 8.2 | 7.6 | 0.6 | CAMHB | inoculum_time_kill | simulated | 1 | 0 | collapse continues |
ABXPD009-TR-0007 | S3 | 1 | Staphylococcus aureus | SA-ATCC29213 | vancomycin | glycopeptide | 1 | 1 | 6 | 4.6 | 1.4 | CAMHB | inoculum_time_kill | simulated | 0 | 0 | baseline inoculum |
ABXPD009-TR-0008 | S3 | 2 | Staphylococcus aureus | SA-ATCC29213 | vancomycin | glycopeptide | 1 | 1 | 7 | 5.8 | 1.2 | CAMHB | inoculum_time_kill | simulated | 0 | 0 | small change |
ABXPD009-TR-0009 | S3 | 3 | Staphylococcus aureus | SA-ATCC29213 | vancomycin | glycopeptide | 1 | 1 | 8 | 7 | 1 | CAMHB | inoculum_time_kill | simulated | 0 | 0 | still proportional |
ABXPD009-TR-0010 | S4 | 1 | Pseudomonas aeruginosa | PA-CLIN610 | ciprofloxacin | fluoroquinolone | 1 | 0.5 | 7 | 6.4 | 0.6 | CAMHB | inoculum_time_kill | simulated | 0 | 0 | single row control |
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
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