Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis Standard Reporting and Evaluation Guidelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Importance: Toxic epidermal necrolysis (TEN) and Stevens-Johnson Syndrome (SJS) are rare, acute, life-threatening dermatologic disorders involving the skin and mucous membranes. Research into these conditions is hampered by a lack of standardization of case reporting and data collection. Objective: To establish a standardized case report form to facilitate comparisons and maintain data quality based on an international panel of SJS/TEN experts who performed a Delphi consensus-building exercise. Evidence Review: The elements presented for committee scrutiny were adapted from previous case report forms and from PubMed literature searches of highly cited manuscripts pertaining to SJS/TEN. The expert opinions and experience of the members of the consensus group were included in the discussion. Findings: Overall, 21 out of 29 experts who were invited to participate in the online Delphi exercise agreed to participate. Surveys at each stage were administered via an online survery software tool. For the first 2 Delphi rounds, results were analyzed using the Interpercentile Range Adjusted for Symmetry method and statements that passed consensus formulated a new case report form. For the third Delphi round, the case report form was presented to the committee, who agreed that it was "appropriate and useful" for documenting cases of SJS/TEN, making it more reliable and valuable for future research endeavors. Conclusions and Relevance: With the consensus of international experts, a case report form for SJS/TEN has been created to help standardize the collection of patient information in future studies and the documentation of individual cases.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it