Minimum Quality Threshold in Pre-Clinical Sepsis Studies (MQTiPSS): an international expert consensus initiative for improvement of animal modeling in sepsis
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
PURPOSE: Pre-clinical animal studies precede the majority of clinical trials. While the clinical sepsis definitions and recommended treatments are regularly updated, a systematic review of pre-clinical models of sepsis has not been done and clear modeling guidelines are lacking. To address this deficit, a Wiggers-Bernard Conference on pre-clinical sepsis modeling was held in Vienna in May, 2017. The conference goal was to identify limitations of pre-clinical sepsis models and to propose a set of guidelines, defined as the "Minimum Quality Threshold in Pre-Clinical Sepsis Studies" (MQTiPSS), to enhance translational value of these models. METHODS: 31 experts from 13 countries participated and were divided into 6 thematic Working Groups (WG): (1) Study Design, (2) Humane modeling, (3) Infection types, (4) Organ failure/dysfunction, (5) Fluid resuscitation and (6) Antimicrobial therapy endpoints. As basis for the MQTiPSS discussions, the participants conducted a literature review of the 260 most highly cited scientific articles on sepsis models (2002-2013). RESULTS: Overall, the participants reached consensus on 29 points; 20 at "recommendation" (R) and 9 at "consideration" (C) strength. This Executive Summary provides a synopsis of the MQTiPSS consensus (Tables 1, 2 and 3). CONCLUSIONS: We believe that these recommendations and considerations will serve to bring a level of standardization to pre-clinical models of sepsis and ultimately improve translation of pre-clinical findings. These guideline points are proposed as "best practices" that should be implemented for animal sepsis models. In order to encourage its wide dissemination, this article is freely accessible in Shock, Infection and Intensive Care Medicine Experimental.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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