Datasets:
row_id string | series_id string | timepoint_h int64 | organism string | strain_id string | antibiotic_name string | antibiotic_class string | drug_conc_mg_L float64 | cfu_log10 float64 | media string | assay_method string | source_type string | shape_distortion_signal int64 | earliest_distortion int64 | notes string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ABXPD005-TR-0001 | S1 | 0 | Escherichia coli | EC-ATCC25922 | meropenem | carbapenem | 2 | 7.2 | CAMHB | time_kill | simulated | 0 | 0 | baseline |
ABXPD005-TR-0002 | S1 | 2 | Escherichia coli | EC-ATCC25922 | meropenem | carbapenem | 2 | 6.3 | CAMHB | time_kill | simulated | 0 | 0 | log linear kill |
ABXPD005-TR-0003 | S1 | 4 | Escherichia coli | EC-ATCC25922 | meropenem | carbapenem | 2 | 5.4 | CAMHB | time_kill | simulated | 0 | 0 | log linear kill |
ABXPD005-TR-0004 | S1 | 6 | Escherichia coli | EC-ATCC25922 | meropenem | carbapenem | 2 | 4.5 | CAMHB | time_kill | simulated | 0 | 0 | log linear kill |
ABXPD005-TR-0005 | S1 | 24 | Escherichia coli | EC-ATCC25922 | meropenem | carbapenem | 2 | 2.7 | CAMHB | time_kill | simulated | 0 | 0 | log linear kill |
ABXPD005-TR-0006 | S2 | 0 | Klebsiella pneumoniae | KP-CLIN061 | ceftriaxone | 3rd_gen_cephalosporin | 16 | 7.1 | CAMHB | time_kill | simulated | 0 | 0 | baseline |
ABXPD005-TR-0007 | S2 | 2 | Klebsiella pneumoniae | KP-CLIN061 | ceftriaxone | 3rd_gen_cephalosporin | 16 | 5.8 | CAMHB | time_kill | simulated | 0 | 0 | fast early kill |
ABXPD005-TR-0008 | S2 | 4 | Klebsiella pneumoniae | KP-CLIN061 | ceftriaxone | 3rd_gen_cephalosporin | 16 | 5.75 | CAMHB | time_kill | simulated | 1 | 1 | plateau begins |
ABXPD005-TR-0009 | S2 | 6 | Klebsiella pneumoniae | KP-CLIN061 | ceftriaxone | 3rd_gen_cephalosporin | 16 | 5.7 | CAMHB | time_kill | simulated | 1 | 0 | plateau persists |
ABXPD005-TR-0010 | S2 | 24 | Klebsiella pneumoniae | KP-CLIN061 | ceftriaxone | 3rd_gen_cephalosporin | 16 | 6.2 | CAMHB | time_kill | simulated | 1 | 0 | regrowth late |
ABX-PD-005: Time-Kill Curve Shape Distortion
This dataset tests whether you can detect loss of log linear kill coherence.
The curve stops behaving like a clean log linear decline.
It becomes plateaued or multiphasic.
Time grid
v1 uses
- 0, 2, 4, 6, 24 hours
Files
- data/train.csv
- data/test.csv
- scorer.py
Schema
Each row is one timepoint in a time kill series.
Required columns
- row_id
- series_id
- timepoint_h
- organism
- strain_id
- antibiotic_name
- antibiotic_class
- drug_conc_mg_L
- cfu_log10
- media
- assay_method
- source_type
- shape_distortion_signal
- earliest_distortion
Labels
shape_distortion_signal
- 1 for rows at or after distortion
earliest_distortion
- 1 only for the first detected row in that series
Evaluation
Run
- python scorer.py --path data/test.csv
The scorer detects distortion using
- plateau segments
- multiphasic sign changes in slopes
- poor linear fit
The scorer avoids a common mistake.
It does not call distortion when drug concentration changes too much.
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