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Record W2807400289 · doi:10.1097/jps.0000000000000169

Comparing Digital Photography via Email Correspondence With Traditional Telephone Communication for Assessment of Postoperative Pediatric Urology Patients

2018· article· en· W2807400289 on OpenAlexaff
Mandy Rickard, Natasha Brownrigg, Kevin Zizzo, Armando J. Lorenzo, Jorge DeMaria, Luis H. Braga

Bibliographic record

VenueJournal of Pediatric Surgical Nursing · 2018
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsSickKids FoundationMcMaster Children's HospitalHospital for Sick Children
Fundersnot available
KeywordsMedicineRandomizationRandomized controlled trialMedical emergencyElectronic data capturePatient satisfactionClinical trialSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Introduction Postoperative concerns are commonly managed by a nurse practitioner (NP) via traditional telephone conversation (TTC). In recent years, electronic interaction, including digital photographs sent via email (PEC), has become an alternative, novel strategy to evaluate surgical site concerns. Its use may result in fewer unplanned clinic or emergency room (ER) visits as well as improve patient satisfaction. Herein, we present a pilot a study to determine the feasibility of conducting a definitive trial comparing the effectiveness of PEC versus TTC in reducing the number of unplanned clinic and ER visits as well as improving patient experience. Materials and Methods Children < 18 years old at the time of surgery and within the 30-day postoperative period were recruited from June 2015 to January 2016 at a tertiary children’s hospital. Exclusion criteria were concerns occurring outside the 30-day postoperative period and inability/unwillingness to email photographs. Patients were allocated to PEC or TTC after initiating contact with the NP through an electronic centralized blocked randomization system. A standardized telephone script was used to gather relevant clinical data for both groups. These data informed a clinical plan, with those randomized to the PEC group sending digital photographs of the surgical site in addition to the traditional telephone call. Within 48 hours, families were sent a link to an electronic survey measuring patient experience using a validated questionnaire. Feasibility data on recruitment rates, compliance with sending photographs, and completing patient experience questionnaires were collected. Secondary outcomes included number of unplanned clinic/ER visits, number of follow-up phone calls, and patient experience scores. Results Of the 328 children who underwent urological procedures during the recruitment period, 215 (66%) consented to participate in the study. Of these, 42(13%) contacted the NP with postoperative concerns and were randomized. Two patients in the PEC group were excluded after randomization (one for contacting on Postoperative Day 31 and one for not sending photographs), resulting in 19 patients in the PEC group and 21 patients in the TTC group. Penile surgeries (hypospadias repair and circumcisions [43%]) were the most common procedures with postoperative concerns. Ninety-eight percent of the PEC patients were compliant in sending photographs. Overall, 98% of surveys were completed. Twice as many unplanned clinic visits were observed in the TTC group when compared with the PEC group (p = .28), despite a similar number of follow-up phone calls between groups (Table 1). Patient experience scores were also comparable in both groups, with families scoring high satisfaction with the experience regardless of the modality of communication. Conclusions A definitive trial examining the effectiveness of PEC versus TTC appears feasible and safe as seen by the high recruitment, photographic compliance, hospital visits, and survey completion rates in this pilot study.

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)0.006
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 score0.008
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.095
GPT teacher head0.384
Teacher spread0.289 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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