Strange Lab

Clinical trials · Full deck · September 2026

Current serving family · September 27

Clinical-trial forecasts

AACT 2.0 combines trial state, sponsor history and current facts to forecast six- and twelve-month outcomes. These are registry outcomes, distinct from the earlier FDA oncology study.

Explore trial event histories · Trial Risk · Conditional scenarios

Outcome forecasts

Forecast ranking results

On six development anchors, August 2025 through January 2026, six-month AUROC was 0.819 for enrollment shortfall, 0.832 for termination and 0.806 for completion. These rank later positive outcomes above negatives; they are not per-trial probabilities.

Six-month targetPrevious familyAACT 2.0
Enrollment shortfall0.8140.819
Termination0.8270.832
Completion0.7890.806

The twelve-month comparison also improved 12 of 14 targets. Withdrawal and results submission declined at both horizons. One seed; development anchors. The served arm includes fresher facts, and a new facts-only rival was not recomputed, so this does not isolate encoder value.

Probabilities and attention

Ranking, calibration and coverage

The serving tools provide outcome probabilities, attention lists and expected counts. Calibration maps are fit on an earlier eligible block and checked on a later reporting block.

The current six-month reporting read has expected calibration error 0.002 for shortfall and 0.001 for termination; positive Brier skill is 0.019 and 0.014 respectively. Small rare-outcome calibration error does not imply strong per-trial certainty.

Registry records do not supply a complete live enrollment feed. Forecasts concern what the registry later records, subject to its publication cadence and source quality.

Conditional scenarios

Conditional scenarios

The current scenario path composes rolled trial state with stepped facts and clock, while holding sponsor context at the history cutoff. It serves the twelve-month production artifact.

A supplied move is compared with the same future date with nothing filed. The difference is a conditional model response, not a causal estimate. Real-event-chain evaluation tests outcome ranking; imagined trajectories remain descriptive.

Open Trial Dynamics