MCID — The Minimal Clinically Important Difference Assigns Significance to Outcome Effects
Bibliographic record
Abstract
At medical school we were taught that 70–80% of the information that leads to diagnosis comes from the patient’s anamnesis. The severity of the subjective symptoms and disabilities drives the patient to seek medical help and has a major influence on treatment and intervention, despite “objective” findings such as alterations on a radiograph, for example, in a knee affected by osteoarthritis (OA)1. Based on this insight, a huge number of patient-rated outcome instruments have been developed in the past 2 decades2. Nevertheless, the significance of therapeutic effects is still quantified by statistical methods alone in many study reports, especially in pharmacological ones, even if the effects are labeled as “clinically significant”3. Beyond statistically and distribution-based quantification of effect significance, the dimension of an effect’s importance and significance, which includes the patient’s subjective perception of pain and function, reaches a higher sphere because it is closer to the central subject of interest in medicine, the patient. “It is recommended that the patient’s perspective be given the most weight, because these are patient-related outcome measures, although the clinician’s perspective is considered important as well”4. The founders of the concepts developed consequently to give this alternative meaning for outcome effects were Jaeschke and Redelmeier5,6. Jaeschke was the first investigator to ask patients to rate their subjectively perceived change of health or symptoms between baseline and followup5. The responses on this “transition” item were related to the score differences of an outcome instrument within the same time period … Address correspondence to Dr. F. Angst, RehaClinic, Quellenstr. 34, 5330 Bad Zurzach, Switzerland; E-mail: fangst{at}vtxmail.ch, f.angst{at}rehaclinic.ch
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.069 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".