Bibliographic record
Abstract
(2000) J Am Coll Surg 191, 227. Wall DB, Klein SR, Black S, et al. . A simple model to help distinguish necrotizing fasciitis from nonnecrotizing soft tissue infection. . Sep; . : . –31 . [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] QUESTION: How accurate is a 2 factor model in differentiating early stage necrotising fasciitis (NF) from other non-necrotising soft tissue infections (NNFs)? The model was derived by using data from a previous case control study, and a retrospective cohort study was used to validate the model. A university medical centre in Torrance, California, USA. Data from 42 patients (mean age 39 y, 81% men, 50% with NF and 50% with NNF) were used for the derivation set. Data from 359 patients (mean age 44 y, 77% men, 9% with NF and 91% with NNF) admitted to hospital between April 1998 and March 1999 with a primary diagnosis of NF or NNF infection at discharge were used for the validation set. More patients with NF had a history of hepatitis (19% … [1]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2Bthe%2BAmerican%2BCollege%2Bof%2BSurgeons%26rft.stitle%253DJ%2BAm%2BColl%2BSurg%26rft.aulast%253DWall%26rft.auinit1%253DD.%2BB.%26rft.volume%253D191%26rft.issue%253D3%26rft.spage%253D227%26rft.epage%253D231%26rft.atitle%253DA%2Bsimple%2Bmodel%2Bto%2Bhelp%2Bdistinguish%2Bnecrotizing%2Bfasciitis%2Bfrom%2Bnonnecrotizing%2Bsoft%2Btissue%2Binfection.%26rft_id%253Dinfo%253Adoi%252F10.1016%252FS1072-7515%252800%252900318-5%26rft_id%253Dinfo%253Apmid%252F10989895%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1016/S1072-7515(00)00318-5&link_type=DOI [3]: /lookup/external-ref?access_num=10989895&link_type=MED&atom=%2Febmed%2F6%2F3%2F96.1.atom [4]: /lookup/external-ref?access_num=000089108200001&link_type=ISI
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".