The HEADS-ED
Bibliographic record
Abstract
OBJECTIVES: This effectiveness study aimed to evaluate the clinical use of the HEADS-ED tool for patients presenting to a pediatric emergency department (PED) for mental health (MH) care. METHODS: In this pragmatic trial, PED physicians used the HEADS-ED to guide their assessment and identify areas of MH need in 639 patients (mean [SD], 15.16 [1.40] years; female, 72.6%) who presented to the emergency department with MH concerns between May 2013 and March 2014. RESULTS: The HEADS-ED guided consultation to psychiatry/crisis, with 86% receiving a recommended consult. Those with a HEADS-ED score of greater than or equal to 8 and suicidality of 2 (relative risk, 2.64; confidence interval, 2.28-3.06) had a 164% increased risk of physicians requesting a consult compared with those with a score of less than 8 or greater than or equal to 8 with no suicidality of 2. The HEADS-ED mean score was significantly higher for those who received a consult (M = 6.91) than those who did not (M = 4.70; P = 0.000). Similarly, the mean score for those admitted was significantly higher (M = 7.21) than those discharged (M = 5.28; P = 0.000). Agreement on needs requiring action between PED physicians and crisis intervention workers was obtained for a subset of 140 patients and ranged from 62% to 93%. CONCLUSIONS: Results support the HEADS-ED's use by PED physicians to help guide the assessment and referral process and for discussing the clinical needs of patients among health care providers using a common action-oriented language.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".