Bibliographic record
Abstract
The problem of postburn itch has been underevaluated and undertreated in the past. However, recently published data have expanded the evidence base, which provides clinicians and their patients with new evaluation and treatment options that can help reduce and potentially eliminate the prolonged distress experienced by burn survivors faced with postburn itch. Although a gold standard evaluation method has not yet been agreed upon, there are a number of tools that have been published that clinicians can use for assessment. Epidemiological evidence has confirmed that the vast majority of both adult and pediatric burn survivors experience itch for years following injury. At discharge from the acute care hospital, 93% of burn survivors with major burn injuries report postburn itch that is still experienced by 44% of adult burn survivors 30 years postburn. Although larger surface area injuries are more likely to require a multimodal treatment approach to reduce the itch intensity as well as the episode duration and frequency, burn survivors with small surface area injuries also experience itch that needs to be addressed. A number of treatment protocols have been described that commonly call for concurrent administration of both pharmacological and nonpharmacological treatment approaches. These protocols provide clinicians with a structured, systematic approach to treatment decisions that are evidence-based. Although many questions require further investigation, the current state of the science creates an ethical imperative that all burn survivors' itch experience should be quantitatively evaluated and appropriate treatment options explored until satisfactory outcomes are obtained.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".