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
- Downloads last month
- 9