Learning the Thyroid Examination-A Multimodality Intervention for Internal Medicine Residents
Bibliographic record
Abstract
BACKGROUND: Many physicians have inadequate physical diagnosis skills and cannot detect thyroid abnormalities on physical examination. PURPOSE: To evaluate a multimodality intervention to improve thyroid examination skills using a prospective controlled trial in first-year residents enrolled in an academic internal medicine program. METHODS: The intervention group received a 60-minute educational session during which an endocrinologist described anatomical landmarks, thyroid abnormalities, and examination techniques using a slide show, computerized animation, videotape, and live demonstration on a volunteer with goiter. Residents examined a normal and a goitrous thyroid under the observation of a preceptor and received an evidence-based handout on the thyroid examination. The control group received no specific intervention. Examination technique and identification of thyroid abnormalities were blindly assessed in 2 stations of an objective structured clinical examination (OSCE). RESULTS: Of the 19 residents in the intervention group and the 20 in the control group, 6 (32%) and 3 (15%), respectively, observed the neck for thyroid abnormalities (P = 0.3), 17 (90%) and 16 (80%) used proper hand position (P = 0.7), and 13 (68%) and 15 (75%) had the patient swallow while the neck was palpated (P = 0.7). There was a significant difference in the mean scores based on thyroid physical findings during the OSCE between the intervention and control groups (100 vs. 52.5 [maximal possible score = 200], P = 0.047). CONCLUSION: A 1-hour multimodality learning session furthered the ability of first-year internal medicine residents to detect thyroid abnormalities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".