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 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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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, 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".