Bibliographic record
Abstract
The topic of the session was Domestic/intimate partner violence injury documentation -Do's and don'ts (Lewis-O'Connor, 2006).The speaker possessed a PhD.The audience was a multinational audience of nurses.In a lecture about documenting injuries, a group of more than 100 forensic nurses could not reach a consensus on how to document each of the injuries described.The one thing upon which everyone agreed at the end of the session was that this is a topic that needs further clarification.A prudent writer would explore this topic alone rather than inviting exposure to criticism for trying to clarify descriptions that are generally understood.Experience suggests, however, that many emergency nurses struggle with the documentation of injuries.Perhaps some terms are not really generally understood.It might be useful to review the more common injuries that emergency nurses see and explore terminology. Emergency nurses are familiar with the acronym T-E-A-R-S.Crowley (1999) identifies the words represented as "tears, ecchymoses, abrasion, redness, and swelling" (p.88).Although there are other mnemonics, this seems to be the most widely used; therefore, it was selected as the basis of an exploration of wound definitions.T: tears (laceration) or tenderness (Giardino, Datner, Asher, Girardin, Faugno, & Spencer, 2003, p. 182).This is generally used to remind nurses of lacerations, probably the most common of all injuries and usually requiring sutures.It is not unusual for emergency nurses to mistakenly chart an injury from a sharp object as a laceration (Assid, 2005).Lacerate means "to tear or rend roughly" (Webster, 2001).Lacerations are injuries that are caused by impact with a blunt object and result from "tearing, ripping, crushing, overstretching, pulling apart, bending and shearing soft tissues (Besant-Matthews, 2006, p. 195).They can range from the fairly tidy open wound, as when wall meets toddler forehead, to a messy, grossly irregular gash caused by closing a door on a finger.On close inspection, jagged edges and bridging across the wound margins may be visible, particularly at the ends of the wound.Bridging refers to the
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.015 |
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".