The FACES of the Future: Emojis in Perioperative Medicine
Bibliographic record
Abstract
Emojis—colorful symbols used to replace or augment text when communicating via digital means—are more than a 21st-century fad: they may have a role in enabling medical care. Anesthesiologists, already adept at using symbols to communicate, rely on the Wong-Baker FACES Pain Rating Scale (FACES-PRS) to help gauge children’s pain levels. One couldn’t be blamed for mistaking a FACES-PRS 0 for the Unicode Consortium’s (UC) “slightly smiling face” emoji (U + 1F642). With emojis being standardized, universal, and familiar cultural staples, they may be able to be used to facilitate the delivery of clinically relevant information.1 This is becoming increasingly relevant as COVID-19 continues to highlight the role of technology in health care, more specifically, the integration of telemedicine in an ever-growing multilingual landscape. Owing to their ability to raise awareness, educate, and portray disease and its management in a manner not restricted by language or education, emojis with a medical context are becoming more common. Currently, there are approximately 30 of these distinctive depictions that could be considered medically relevant. In 2020, 15 medical emojis were proposed to the UC, representing an increase of 50%.1 The proposal process entails submitting a sample to the UC with an explanation detailing its need, uniqueness, and versatility. Anesthesiologists—experts in critical care medicine, pain management, and the perioperative space—may use an assortment of instruments in their everyday work: laryngoscopes, tracheal tubes, laryngeal masks, intravenous (IV) lines, bag valve masks, or sharps disposal containers. To date, none of these have been formally captured by the UC in an emoji format despite being used by other health care professionals including, but not limited to, respirologists, emergency medicine physicians, intensivists, nurses, respiratory therapists, perfusionists, phlebotomists, paramedics, and veterinarians. The Figure illustrates a timeline in which various medical emojis were added to the UC and widely adopted by major vendors (eg, Apple), as well as renderings of a Macintosh laryngoscope, tracheal tube, laryngeal mask, IV line, bag valve mask, an anesthetized individual, a member of the perioperative team, and sharps disposal containers that could be referenced in future emoji design. The sharps disposal container is portrayed as both red (hexadecimal color code FF0000) (standard in the United States) and yellow (hexadecimal color code FFFF00) (standard in Europe) due to variability in waste management regulations.2Figure.: Acceptance of a sample of medical emojis (by year) as per the United Consortium. Emojis denoted by ** are both identified as “syringe” (U + 1F489). The syringe emoji, originally introduced in 2015, underwent a cosmetic change in 2021 and gained the identifier “vaccination” alongside its original moniker. Renderings for nonpublished emojis are as follows. A, Macintosh laryngoscope (“laryngoscope”). B, Bag valve mask. C, Sharps disposal container (US variant). D, Tracheal tube. E, Laryngeal mask. F, Sharps disposal container (Europe variant). G, IV line. H, Anesthetized individual. I, Perioperative team member. IV indicates intravenous.In increasingly global communities, language can be a barrier to care, collaboration, and campaigns.3 Emojis can be used to increase understanding in these scenarios and may be particularly useful when children or adolescents are a target audience.4 With emojis continuing to transform day-to-day interactions, even in the clinical environment, a suite of emojis applicable to anesthesia (and other health care professions) may prove useful when communicating with patients, team members, and via public health messaging, especially in international settings. Acknowledging a need for, and the rendering of, 9 such emojis is the first step toward a future where these emojis exist—the refinement of these renderings and the drafting of a proposal to the UC for the next cycle are the next. E
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".