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Record W4389233951 · doi:10.1182/blood-2023-177767

Are Medical Learners Adept at Recognizing Heavy Vaginal Bleeding?

2023· article· en· W4389233951 on OpenAlexaff
Fartoon M. Siad, Filomena Meffe, Andrea Lausman, Carolyn Snider, Martina Trinkaus, Michelle Sholzberg

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFeelingFamily medicineMedicineObstetrics and gynaecologyDescriptive statisticsPsychologyMedical educationPregnancy

Abstract

fetched live from OpenAlex

Background - More than half of reproductive-age females experience heavy vaginal blood loss (VBL), whether menstrual, lochial, or otherwise, and yet less than 10% seek medical attention for evaluation. Moreover, there is evidence that even when females present to medical attention that heavy VBL is serially under-recognized and untreated. Complications from heavy VBL adversely impact health-related quality of life. This makes characterizing and quantifying VBL critical. It is well documented that there are widespread knowledge and care gaps surrounding VBL due to structural multidimensional stigma. Importantly, it is unclear the extent to which medical learners confidently recognize symptoms and signs of heavy VBL. Objectives - To explore the understanding, attitudes, and perceptions of medical learners in characterizing VBL using an online survey. Methods - An online survey was launched to assess trainees' ability to identify heavy VBL. Survey questions were informed by the literature and tested for validity. The survey was distributed online to trainees working at all local University affiliated hospitals. Results were analyzed using descriptive statistics. Institutional ethics approval was obtained. Results - 73 learners (medical students, residents, and fellows) completed the survey. 88% of learners were between the ages of 25-34, and 56% self-identified as women. 27% were 3rd /4th level medical students, 30% were 2nd year residents, 43% 4th year residents or higher. 27% of residents were from Emergency Medicine, 14% Hematology, 12% Family Medicine, 10% Internal Medicine, and 10% Obstetrics and Gynecology. 94% of trainees reported asking about heavy VBL in the last 6 months. Most described feeling comfortable asking about and diagnosing VBL regardless of cultural, religious or ethnic background, sexual orientation, or gender. 56% were aware of bleeding assessment tools (BATs), 51% of menstrual cups, 21% of the pictorial bleeding assessment chart (PBAC) however, less than 60% had used BATs, less than 75% had used menstrual cups and less than 80% had used the PBAC in clinical practice (Figure 1). Overall, trainees acknowledged stigma surrounding iron deficiency without anemia, and that iron deficiency was associated with decreased health-related quality of life. They recognized the need to screen and not rely on patients being forthcoming about excessive VBL. Discussion - Heavy VBL is exceedingly common, has important clinical and psychosocial ramifications, yet it continues to be stigmatized, underdiagnosed, and thus poorly treated. We found that while a broad range of medical learners described themselves as largely feeling comfortable with their skills in assessing VBL and being aware of tools to facilitate the diagnosis of heavy VBL, surprisingly few had used these tools in clinical practice. Our findings highlight important knowledge gaps surrounding VBL and interestingly, show excessive confidence in its diagnosis amongst medical learners. Targeted knowledge translation rooted in theory- and evidence-based implementation science is urgently required in this space. Next steps involve assessing trainee skills in practice and exploring patient lived experiences with VBL through qualitative interviews.

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)0.003
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.043
GPT teacher head0.306
Teacher spread0.263 · 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 designObservational
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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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