Improving the Otolaryngology Consultation Service in a Teaching Hospital
Bibliographic record
Abstract
OBJECTIVE: To examine the type and quality of consultations requested from the otolaryngology service at a tertiary care hospital. STUDY DESIGN: Retrospective. METHOD: Review of written documentation of consultations over a 6-month period. RESULTS: One hundred eleven requests were received, and 107 written reports were made. Twenty services made requests. Thirty-two percent of requests had a legible requester or contact listed. Sixty-seven percent of requests stated why the patient was in hospital, and 85% stated the otolaryngological complaint. Thirty-two percent of requests made accurate reference to the otolaryngological history, and 6% recorded an ENT examination that was accurate. Seven percent of patients were intubated, and 16% had a tracheostomy prior to evaluation. Forty-eight percent of patients required flexible nasopharyngolaryngoscopy. Sixteen percent of patients required rhinoscopy, and 16% required tracheotomy. Twelve percent of patients needed audiograms, and small numbers of patients required biopsy, debridement of ears, ventilation tube insertion, nasal packing, or radiological studies. Reports were made by senior residents, and evidence that the case was discussed with or seen by an attending surgeon was present in 43% of reports. A diagnosis was stated in 85% of reports, and in 3% the diagnosis appeared to be inaccurate as compared with the history and physical examination recorded. A follow-up plan was stated in 70% of reports. CONCLUSIONS: There is a need to educate physicians about collegial communication regarding patients. This information can direct curriculum needed to prepare otolaryngology residents to provide a consultative service in a teaching hospital. This method of determining "true learning needs" can be used in other situations to improve resident training.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".