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A Research Laboratory Methods Course for Medical Subspecialty Residents

2002· article· en· W1992853487 on OpenAlexaff
Heather Lochnan, ROBERT HACHE

Bibliographic record

VenueAcademic Medicine · 2002
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of OttawaOttawa HospitalMedical Council of Canada
Fundersnot available
KeywordsSubspecialtyTechnicianMedical educationCommitRelevance (law)Principal (computer security)MedicinePsychologyFamily medicineComputer scienceEngineering

Abstract

fetched live from OpenAlex

Objective: The teaching of basic sciences to medical residents remains an ongoing need. Recent advances in science such as the Human Genome Project reinforce the importance and clinical relevance of the basic sciences. Beyond the teaching of basic sciences, residents need to have some understanding of the research laboratory methods commonly used and their limitations. Residents in medical subspecialties hail from various backgrounds, and the majority have had no exposure to a research laboratory. The Research Laboratory Methods Course was designed to allow residents a hands-on opportunity to work in four basic science laboratories. The goal is to provide a unique experience in the laboratory without a long-term commitment to a research project. Residents will acquire hands-on knowledge of commonly used techniques and an understanding of the research being carried out in the laboratory. Issues related to limitations of the techniques, relevant controls, and interpretation of results and safety concerns will be discussed. Upon completion, residents will also be familiar with biotechnical manuals and databases for future reference. Description: A catalog of participating laboratories is compiled detailing the specific experimental objectives for each day of the weekly blocks. Each principal investigator is asked to commit to accepting a resident for one week, to be closely involved in the evaluation process, and to ensure that a technician is assigned to work with the resident. Principal investigators are members of the division; some are PhD scientists and some are clinician scientists. The subspecialty resident will attend the course, designed to include one week in each of the four different laboratories. The resident will be excused from normal daytime duties with the exception of the longitudinal clinic. An information package detailing objectives of the course, methods of evaluation, and where to report is provided. An information package is also provided to the principal investigator outlining his or her responsibilities and methods of evaluation. Discussion: This course has been successfully completed by four endocrinology training residents and will be provided to all our new training residents. In all but one of the weekly blocks, proposed experiments were successfully completed within the allotted times. The residents reported they did the majority of the work and felt well supervised, with no safety concerns. They appreciated the opportunity to meet staff in the division who are often considered inaccessible. Review of the evaluations did not identify any problems. The overall satisfaction level was very high, and the principal investigators involved were willing to continue participating in the future. The experience of attending four laboratories provided residents with a unique introduction to different techniques used in laboratory research. The residents reported that they enjoyed the structured curriculum and the opportunity to sample a number of techniques in different laboratories. The actual experimental protocols were easy to create and were based on techniques commonly used in molecular biology. This course could easily be implemented for subspecialty residents using the laboratories of researchers within the department.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.149
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0030.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1490.084

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.

Opus teacher head0.095
GPT teacher head0.451
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2002
Admission routes1
Has abstractyes

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