New treatments and new assessment instruments for Hidradenitis suppurativa
Bibliographic record
Abstract
Research interest in Hidradenitis Suppurativa (HS) has grown exponentially over the past decades. Several groups have worked to develop novel scores that address the drawbacks of existing investigator-assessed and patient-reported outcome measures currently used in HS trials, clinical practice and research. In clinical trial settings, the drawbacks of the HiSCR have become apparent; mainly, it is lack of a dynamic measurement of draining tunnels. The newly developed (dichotomous) IHS4 and HASI-R are backed up by adequate validation data and are good contenders to become the new primary outcome measure in HS clinical trials. Patient-reported outcomes, as well as physician reported measures, are being developed by the HIdradenitis SuppuraTiva cORe outcomes set International Collaboration (HISTORIC). For example, the Hidradenitis Suppurativa Quality of Life (HiSQOL) score is a validated measure of HS-specific quality of life and is already being used in many HS trials. Magnitude of pain measurement via a 0-10 numerical rating scale is well-established; however, consensus is still required to ensure consistent administration and interpretation of the instrument. A longitudinal measurement over multiple days rather than at one time point, such as for example the Pain Index could provide increased reliability and reduced recall bias. Ultimately, these newly developed scores and tools can be included in a standardized registry to be used in routine clinical practice.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".