Datasets:
row_id string | series_id string | timepoint_d int64 | cycle_id int64 | drug_in_cycle string | stress_index float64 | cycle_exposure_index float64 | population_resistance_burden float64 | expected_burden_under_cycling float64 | burden_gap float64 | mdr_marker float64 | mic_to_active_drug_fold_vs_baseline float64 | clinical_failure_flag int64 | source_type string | cycling_collapse_signal int64 | earliest_cycling_collapse int64 | notes string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ABXCT010-TR-0001 | S1 | 0 | 1 | A | 0.1 | 0.2 | 0.1 | 0.1 | 0 | 0.1 | 1 | 0 | simulated | 0 | 0 | baseline |
ABXCT010-TR-0002 | S1 | 7 | 2 | B | 0.9 | 0.9 | 0.12 | 0.1 | 0.02 | 0.12 | 1 | 0 | simulated | 0 | 0 | cycling works |
ABXCT010-TR-0003 | S1 | 14 | 3 | C | 0.9 | 0.9 | 0.35 | 0.12 | 0.23 | 0.55 | 1.1 | 0 | simulated | 0 | 0 | first drift |
ABXCT010-TR-0004 | S1 | 21 | 4 | A | 0.9 | 0.9 | 0.42 | 0.12 | 0.3 | 0.62 | 1.2 | 0 | simulated | 1 | 1 | confirmed collapse onset |
ABXCT010-TR-0005 | S1 | 28 | 5 | B | 0.9 | 0.9 | 0.55 | 0.12 | 0.43 | 0.75 | 1.4 | 0 | simulated | 1 | 0 | collapse persists |
ABXCT010-TR-0006 | S1 | 42 | 7 | A | 0.9 | 0.9 | 0.7 | 0.15 | 0.55 | 0.85 | 2.5 | 1 | simulated | 1 | 0 | clinical failure later |
ABXCT010-TR-0007 | S2 | 0 | 1 | A | 0.1 | 0.2 | 0.1 | 0.1 | 0 | 0.1 | 1 | 0 | simulated | 0 | 0 | baseline |
ABXCT010-TR-0008 | S2 | 7 | 2 | B | 0.9 | 0.9 | 0.12 | 0.1 | 0.02 | 0.12 | 1 | 0 | simulated | 0 | 0 | stable |
ABXCT010-TR-0009 | S2 | 14 | 3 | C | 0.9 | 0.9 | 0.13 | 0.1 | 0.03 | 0.12 | 1 | 0 | simulated | 0 | 0 | no collapse |
ABXCT010-TR-0010 | S3 | 0 | 1 | A | 0.3 | 0.9 | 0.4 | 0.1 | 0.3 | 0.6 | 1.2 | 1 | simulated | 0 | 0 | stress low |
ABX-CT-010 Cycling Strategy Collapse
Purpose
Detect when antibiotic rotation stops controlling resistance.
Core pattern
- stress_index high
- cycle_exposure_index high
- burden_gap stays high across different cycle drugs
- mdr_marker rises and stays high
- later clinical_failure_flag appears
Files
- data/train.csv
- data/test.csv
- scorer.py
Schema
Each row is one cycle timepoint in a within series rotation history.
Required columns
- row_id
- series_id
- timepoint_d
- cycle_id
- drug_in_cycle
- stress_index
- cycle_exposure_index
- population_resistance_burden
- expected_burden_under_cycling
- burden_gap
- mdr_marker
- mic_to_active_drug_fold_vs_baseline
- clinical_failure_flag
- source_type
- cycling_collapse_signal
- earliest_cycling_collapse
Labels
cycling_collapse_signal
- 1 for rows at or after first confirmed collapse point
earliest_cycling_collapse
- 1 only for the first collapse row in that series
Scorer logic in v1
- candidate collapse point
- stress_index at least 0.80
- cycle_exposure_index at least 0.80
- burden_gap at least 0.25 at two consecutive points
- consecutive points must be different drug_in_cycle values
- mdr_marker at least 0.60 and up at least 0.10 vs baseline
- exclude burden_gap spike then snapback artifacts
- confirmation
- clinical_failure_flag equals 1 later in series
Evaluation
Run
- python scorer.py --path data/test.csv
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