Study the past to divine the future. Confucius' wisdom doesn't work for idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Predicting the future is one of the greatest challenges and for many people one of the greatest hopes of humanity. This applies to any aspect of human life and medicine included. In respiratory medicine, predicting the future is particularly difficult for chronic remodelling disorders, such as pulmonary hypertension or fibrosis.1 The course and recovery from an acute illness are usually easier to foresee than the progression and rate of decline for chronic diseases. In particular, one of the major current challenges is actually predicting the effect of the available pharmacological treatments on the course of idiopathic pulmonary fibrosis (IPF), which is of paramount importance but is still rarely possible. Nonetheless, the more we enter the era of the so-called personalised medicine , anticipating the response to a specific drug is becoming part of realistic expectations.2 Safety and efficacy of drugs are assessed in the context of placebo-controlled randomised clinical trials (RCTs). Although a well-established and worldwide accepted methodology, RCTs still have limitations: one of these is the fact that necessarily trials last for a definite period of time, for IPF typically 12 months, during which time all participants are blinded to the active treatment or to a placebo. This limitation is intrinsic and unavoidable, given the need to balance between harm and benefit when new drugs with unknown effects are tested in patients. However, once approved, all new drugs undergo a mandatory postapproval surveillance of several years. While this type of postmarketing surveillance provides valid information about long-term safety of new drugs, there is no formal way of assessing long-term efficacy, and even if these studies report on efficacy, they are never controlled and therefore the evidence base is less rigorous than for prospective trials. For this reason, other forms of clinical research may be used to inform clinical …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".